{"version":1,"pages":[{"id":"aGpmycHjhiiBYRKJPNA1","title":"Welcome","pathname":"/","siteSpaceId":"sitesp_yCxu9","description":"Introduction of the RagaAI - An end to end testing platform for GenerativeAI and DiscriminativeAI models - LLM, Computer Vision, NLP and Tabular Data."},{"id":"2VSG3MJAfUu3VVnUnu0S","title":"RagaAI Catalyst","pathname":"/ragaai-catalyst","siteSpaceId":"sitesp_yCxu9","description":"The automated AI evaluation platform to build safe, reliable and cost efficient GenAI applications."},{"id":"o7ayJOWm50qf75y3SsbF","title":"User Quickstart","pathname":"/ragaai-catalyst/user-quickstart","siteSpaceId":"sitesp_yCxu9","description":"This guide will help you get started with RagaAI Catalyst using any sample dataset, straight from the RagaAI GUI or the Python environment.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"rTFVIvPO3USYGsGY5hJ9","title":"Concepts","pathname":"/ragaai-catalyst/concepts","siteSpaceId":"sitesp_yCxu9","description":"RagaAI Catalyst Concepts – GenAI Evaluation & Observability Overview","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"l3qQJ5Fodl6XwIHmzNpu","title":"Configure Your API Keys","pathname":"/ragaai-catalyst/concepts/configure-your-api-keys","siteSpaceId":"sitesp_yCxu9","description":"Set API keys for your LLM models to enable metric evaluations on the Catalyst platform. If you're trying to set up a custom gateway, refer the \"Enable Custom Gateway\" page.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"ry4Yxjao2iuPwbLyFYXA","title":"Supported LLMs","pathname":"/ragaai-catalyst/concepts/supported-llms","siteSpaceId":"sitesp_yCxu9","description":"Supported LLMs – Compatible Models in RagaAI Catalyst","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"i7wyPYO197z3IUuAqA2K","title":"OpenAI","pathname":"/ragaai-catalyst/concepts/supported-llms/openai","siteSpaceId":"sitesp_yCxu9","description":"OpenAI Integration – RagaAI Catalyst Supported LLM","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Supported LLMs"}]},{"id":"s0dsyFET4TGsIDSJgksW","title":"Gemini","pathname":"/ragaai-catalyst/concepts/supported-llms/gemini","siteSpaceId":"sitesp_yCxu9","description":"Google Gemini – RagaAI Catalyst Supported LLM","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Supported LLMs"}]},{"id":"A0YeghdjgD5027i7Cc85","title":"Azure","pathname":"/ragaai-catalyst/concepts/supported-llms/azure","siteSpaceId":"sitesp_yCxu9","description":"Azure OpenAI – RagaAI Catalyst Supported LLM","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Supported LLMs"}]},{"id":"cn2VU5euO39HIUieTiON","title":"AWS Bedrock","pathname":"/ragaai-catalyst/concepts/supported-llms/aws-bedrock","siteSpaceId":"sitesp_yCxu9","description":"AWS Bedrock – RagaAI Catalyst Supported LLM","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Supported LLMs"}]},{"id":"5DCLAnoUKqyujkdy1B0D","title":"ANTHROPIC","pathname":"/ragaai-catalyst/concepts/supported-llms/anthropic","siteSpaceId":"sitesp_yCxu9","description":"Anthropic Claude – RagaAI Catalyst Supported LLM","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Supported LLMs"}]},{"id":"kLWnQ5lofpkuGFWfzS65","title":"Catalyst Access/Secret Keys","pathname":"/ragaai-catalyst/concepts/catalyst-access-secret-keys","siteSpaceId":"sitesp_yCxu9","description":"Generate access and secret keys and quickly integrate Catalyst within your application's SDK flow.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"Sdvn9CjTZGdRrVb4RP6H","title":"Enable Custom Gateway","pathname":"/ragaai-catalyst/concepts/enable-custom-gateway","siteSpaceId":"sitesp_yCxu9","description":"Run metric evaluations on your own LLM models, even if you use a custom gateway.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"6NaEvgJx0OfgKWEMPnZZ","title":"Uploading Data","pathname":"/ragaai-catalyst/concepts/uploading-data","siteSpaceId":"sitesp_yCxu9","description":"Understand the process of creating a new project and importing your dataset, including file format requirements and tips to prepare your data for effective LLM testing in the platform.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"hqAyeSMwcnDjTVJjAIJA","title":"Create new project","pathname":"/ragaai-catalyst/concepts/uploading-data/create-new-project","siteSpaceId":"sitesp_yCxu9","description":"Get started on evaluations by creating use-cases specific projects","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"yUhyWANCAvHjkti1sGia","title":"RAG Dataset","pathname":"/ragaai-catalyst/concepts/uploading-data/rag-datset","siteSpaceId":"sitesp_yCxu9","description":"Once your project is created, you can upload datasets to it for evaluation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"UA0ejc6xqxPspqeS8v0y","title":"Chat Dataset","pathname":"/ragaai-catalyst/concepts/uploading-data/chat-dataset","siteSpaceId":"sitesp_yCxu9","description":"Guide for uploading chat datasets to RagaAI Catalyst","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"mYqF66hwyYdQCXjrIrC0","title":"Prompt Format","pathname":"/ragaai-catalyst/concepts/uploading-data/chat-dataset/prompt-format","siteSpaceId":"sitesp_yCxu9","description":"How to represent conversation prompts and responses when preparing your dataset, ensuring Catalyst correctly interprets each user query and assistant reply during evaluation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"},{"label":"Chat Dataset"}]},{"id":"6iPEWiNUD64ZbCXjmOq7","title":"Logging traces (LlamaIndex, Langchain, etc.)","pathname":"/ragaai-catalyst/concepts/uploading-data/logging-traces","siteSpaceId":"sitesp_yCxu9","description":"Learn how to log execution traces from LlamaIndex or LangChain into RagaAI Catalyst. Capture prompts, responses, and steps for easier debugging and evaluation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"7CVspIwzuhkwfXiRIzcy","title":"Trace Masking Functions","pathname":"/ragaai-catalyst/concepts/uploading-data/trace-masking-functions","siteSpaceId":"sitesp_yCxu9","description":"Users can mask keywords and regex patterns using custom Python functions","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"GJSX2CiyGQiBcZj0BXz9","title":"Trace Level Metadata","pathname":"/ragaai-catalyst/concepts/uploading-data/trace-level-metadata","siteSpaceId":"sitesp_yCxu9","description":"Add metadata as key-value pairs for your real-time trace logs","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"O8qDFGRZHMaaDGSUUfUk","title":"Correlating Traces with External IDs","pathname":"/ragaai-catalyst/concepts/uploading-data/correlating-traces-with-external-ids","siteSpaceId":"sitesp_yCxu9","description":"Users can connect the traces logged on RagaAI Catalyst with their existing logs elsewhere - such as GCP, AWS, or other internal services","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"kZMaVLJBvEstZ0oKDkz7","title":"Add Dataset","pathname":"/ragaai-catalyst/concepts/uploading-data/add-dataset","siteSpaceId":"sitesp_yCxu9","description":"How to upload or create datasets for RAG, chat testing, or custom evaluations. Follow steps for import and format verification.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Uploading Data"}]},{"id":"eH6nR1UrqntmIQxiLwOF","title":"Running RagaAI Evals","pathname":"/ragaai-catalyst/concepts/running-ragaai-evals","siteSpaceId":"sitesp_yCxu9","description":"This section contains all information required by users to run RagaAI's automated evaluation metrics as well as guardrails.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"aoXppxWJKkOQuEupyguP","title":"Executing Evaluations","pathname":"/ragaai-catalyst/concepts/running-ragaai-evals/executing-evaluations","siteSpaceId":"sitesp_yCxu9","description":"Run various cutting edge evaluation metrics out-of-the-box with a few simple steps","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Running RagaAI Evals"}]},{"id":"tC2AWaOH4dwuFeCDEcBz","title":"Compare Datasets","pathname":"/ragaai-catalyst/concepts/running-ragaai-evals/compare-datasets","siteSpaceId":"sitesp_yCxu9","description":"Compare results from multiple datasets or runs side by side. Use diff view to spot performance differences across models and dataset versions easily.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"},{"label":"Running RagaAI Evals"}]},{"id":"PCZ1CKpxfV4zBFv7yYqO","title":"Analysis","pathname":"/ragaai-catalyst/concepts/analysis","siteSpaceId":"sitesp_yCxu9","description":"RagaAI Catalyst’s analysis dashboard helps interpret evaluation results with metrics, charts, and visualizations. Drill into test cases, spot failure patterns and gain insights to improve your LLM.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"l1qbEQB6b7cXWR3fYP22","title":"Embeddings","pathname":"/ragaai-catalyst/concepts/embeddings","siteSpaceId":"sitesp_yCxu9","description":"Explore how RagaAI Catalyst uses vector embeddings for semantic analysis. Visualize spaces, compare query–document similarity, and apply metrics to evaluate LLM retrieval quality.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Concepts"}]},{"id":"g4vtPrJIHndl3Gr6D6bs","title":"RagaAI Metric Library","pathname":"/ragaai-catalyst/ragaai-metric-library","siteSpaceId":"sitesp_yCxu9","description":"This section highlights all the different kinds of evaluation metrics and guardrails available on the RagaAI platform.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"shuQjyGiqWOgzxbjOeKm","title":"RAG Metrics","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics","siteSpaceId":"sitesp_yCxu9","description":"Overview of RAG metrics in the RagaAI Metric Library. Measure hallucination, faithfulness, and context use to assess LLM accuracy and reliability with external knowledge.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"2Oagob0z0SwhJbXnYfR1","title":"Hallucination RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/hallucination","siteSpaceId":"sitesp_yCxu9","description":"Check if an LLM adds unsupported or fabricated details. A high hallucination score flags ungrounded content and helps developers spot false outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"Fvget4S5rVXKvVUO46My","title":"Faithfulness RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/faithfulness","siteSpaceId":"sitesp_yCxu9","description":"Measure how well an LLM’s answer stays true to the source material. Ensure factual accuracy in RAG by avoiding distortions or errors.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"CX8dvasPYAJnwt00PQ2G","title":"Response Correctness RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/response-correctness","siteSpaceId":"sitesp_yCxu9","description":"Assess if the LLM’s response directly answers the user’s question using context. Evaluate accuracy to check if the model found the right information.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"MoNqSe9zBQynUlNx5yoc","title":"Response Completeness RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/response-completeness","siteSpaceId":"sitesp_yCxu9","description":"Check if the LLM’s answer fully addresses the question. Ensures responses cover all key points without missing details when context allows.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"QqgSia050VGa7wYhKdjG","title":"False Refusal RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/false-refusal","siteSpaceId":"sitesp_yCxu9","description":"Identify the instances where LLM improperly refuses to answer a question despite having sufficient information to do so. Understand how to fine-tune overly cautious or misaligned refusal behaviors.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"VcRv39KTByT42VhYURjF","title":"Context Relevancy RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/context-relevancy","siteSpaceId":"sitesp_yCxu9","description":"Evaluate how relevant the retrieved context is to the user’s query and the LLM’s answer, ensuring the model uses appropriate and useful information.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"3DyOhfOlt4SdPtJhL8h6","title":"Context Precision RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/context-precision","siteSpaceId":"sitesp_yCxu9","description":"Measure how much of the provided context was relevant to the answer. High context precision means the model used only the most relevant parts with minimal extra info.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"D3MeTWm5vilE3tOB4pEo","title":"Context Recall RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/context-recall","siteSpaceId":"sitesp_yCxu9","description":"Assess how well the LLM used relevant context. High recall means it captured key details without missing important information.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"VwcLLDhDCP5zaUeeEPHd","title":"PII Detection RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/pii-detection","siteSpaceId":"sitesp_yCxu9","description":"Check LLM outputs for Personally Identifiable Information (names, emails, phone numbers) to prevent sensitive data leaks and ensure privacy compliance.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"ZiBhkh8CbEWuX2FbweVP","title":"Toxicity RAG Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/rag-metrics/toxicity","siteSpaceId":"sitesp_yCxu9","description":"Evaluate LLM responses for toxic or offensive language. Flag unsafe outputs and apply filters to ensure safe, respectful interactions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"RAG Metrics"}]},{"id":"byPJGC0GzF0btbu7jgtH","title":"Chat Metrics","pathname":"/ragaai-catalyst/ragaai-metric-library/chat-metrics","siteSpaceId":"sitesp_yCxu9","description":"Chat Metrics in the RagaAI Metric Library evaluate conversational AI quality. Measuring instruction adherence, user clarity, agent helpfulness, coherence, and overall dialogue performance.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"AGMzo5LcsR4zAmqRKGCC","title":"Agent Quality Chat Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/chat-metrics/agent-quality","siteSpaceId":"sitesp_yCxu9","description":"Evaluate the AI assistant’s performance based on helpfulness, accuracy, and appropriateness across the full conversation, giving a holistic engagement score.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Chat Metrics"}]},{"id":"NPHVJUZWB2TZr3oROWT8","title":"Instruction Adherence Chat Metric","pathname":"/ragaai-catalyst/ragaai-metric-library/chat-metrics/instruction-adherence","siteSpaceId":"sitesp_yCxu9","description":"Evaluate how well the AI follows instructions and system prompts. Ensure responses align with user requests, formats, and style guidelines.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Chat Metrics"}]},{"id":"H0Oajr7dEDdN1xkOMQww","title":"User Chat Quality","pathname":"/ragaai-catalyst/ragaai-metric-library/chat-metrics/user-chat-quality","siteSpaceId":"sitesp_yCxu9","description":"Evaluate if user messages are clear, well-formed, and relevant. High-quality inputs enable better AI answers, while unclear queries may need refinement.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Chat Metrics"}]},{"id":"0kyFQbASqZMaLCLv1ZtJ","title":"Text-to-SQL","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql","siteSpaceId":"sitesp_yCxu9","description":"Overview of Text-to-SQL metrics in RagaAI Metric Library, covering SQL correctness, query ambiguity, and alignment with user intent.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"9YkvKc3J95jEamrGbbtl","title":"SQL Response Correctness","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql/sql-response-correctness","siteSpaceId":"sitesp_yCxu9","description":"Check if the LLM-generated SQL query retrieves the intended data. Evaluate accuracy of the model’s NL-to-SQL conversion against expected results.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text-to-SQL"}]},{"id":"nwA6WVIcIHsYDj8oySH9","title":"SQL Prompt Ambiguity","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql/sql-prompt-ambiguity","siteSpaceId":"sitesp_yCxu9","description":"Assess ambiguity in Text-to-SQL queries. High scores show questions that are vague or multi-interpretable, helping flag those needing rephrasing or more context.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text-to-SQL"}]},{"id":"JHtQS2t1cnrYhQpLsVWj","title":"SQL Context Ambiguity","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql/sql-context-ambiguity","siteSpaceId":"sitesp_yCxu9","description":"Evaluate ambiguity in Text-to-SQL tasks. Check if unclear schema elements (tables, columns, etc.) cause multiple interpretations, requiring clearer context.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text-to-SQL"}]},{"id":"VJM6DNeA1GXj5njxH8Ki","title":"SQL Context Sufficiency","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql/sql-context-sufficiency","siteSpaceId":"sitesp_yCxu9","description":"Check whether the SQL context provided is enough for accurate answers. Learn to optimize context and schema inputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text-to-SQL"}]},{"id":"yC7uAmrfdkNNl0r65PgA","title":"SQL Prompt Injection","pathname":"/ragaai-catalyst/ragaai-metric-library/text-to-sql/sql-prompt-injection","siteSpaceId":"sitesp_yCxu9","description":"Protect against prompt injection in SQL tasks. Detect malicious instructions and enforce guardrails for safer execution.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text-to-SQL"}]},{"id":"6m6pP8f3oLSbBguXvRdE","title":"Text Summarization","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization","siteSpaceId":"sitesp_yCxu9","description":"Evaluate LLM-generated summaries for accuracy and readability. Learn to refine summarization for clarity and completeness.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"mw9J0eJtFbwB86IRIA9N","title":"Summary Consistency","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/summary-consistency","siteSpaceId":"sitesp_yCxu9","description":"Measure whether summaries stay faithful to the original text. Spot inconsistencies and improve alignment with source content.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"psjJk3mcrC0wbashaxoc","title":"Summary Relevance","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/summary-relevance","siteSpaceId":"sitesp_yCxu9","description":"Assess if generated summaries include key points. Identify missing elements and ensure better focus on essentials.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"cuhKFRY5TYQbnlI7H4Ss","title":"Summary Fluency","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/summary-fluency","siteSpaceId":"sitesp_yCxu9","description":"Analyze the readability and flow of summaries. Improve grammar and style for more natural outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"0RTqp97CNBYYGPEJh3Mx","title":"Summary Coherence","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/summary-coherence","siteSpaceId":"sitesp_yCxu9","description":"Check if summaries are logically connected and structured. Spot gaps in flow and enhance overall quality.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"quAGnSHnPSAfGejfnWSm","title":"SummaC","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/summac","siteSpaceId":"sitesp_yCxu9","description":"Use SummaC to validate summary alignment with source. Ensure factual and semantic consistency in AI-generated outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"mjAJUJ4CkEh7ehhyUg96","title":"QAG Score","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/qag-score","siteSpaceId":"sitesp_yCxu9","description":"Apply QAG Score to measure summary reliability. Detect factual gaps and improve question-answer consistency.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"N7s6mWuDOtUzLujDXFYx","title":"ROUGE","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/rouge","siteSpaceId":"sitesp_yCxu9","description":"Evaluate summaries with ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics. Measure overlap with references to track relevance and coverage.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"Kdin6f1beJtzgFAEERM7","title":"BLEU","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/bleu","siteSpaceId":"sitesp_yCxu9","description":"Assess LLM text outputs with BLEU (Bilingual Evaluation Understudy). Compare n-gram overlap for translation and summarization accuracy.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"E6dahgMkOQmJWMKBhacH","title":"METEOR","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/meteor","siteSpaceId":"sitesp_yCxu9","description":"Improve evaluation with METEOR (Metric for Evaluation of Translation with Explicit Ordering) scores. Capture synonyms, stemming, and precision/recall beyond BLEU or ROUGE.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"LOfhPRTmCeuYY0K43eNQ","title":"BERTScore","pathname":"/ragaai-catalyst/ragaai-metric-library/text-summarization/bertscore","siteSpaceId":"sitesp_yCxu9","description":"Use BERTScore to check semantic similarity in outputs. Ensure summaries align with original meaning beyond surface words.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Text Summarization"}]},{"id":"75KgZjAo90SHgep44oPO","title":"Information Extraction","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction","siteSpaceId":"sitesp_yCxu9","description":"Evaluate how well LLMs extract information from text. Detect missed entities or relations and enhance precision.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"zGqdxtq0CVI7rRONOgT3","title":"MINEA","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/minea","siteSpaceId":"sitesp_yCxu9","description":"Apply MINEA (Multiple Infused Needle Extraction Accuracy) to test subjective question correction. Identify biases and improve fairness in LLM responses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"BjIxwXbD9T7eP2AN2bLp","title":"Subjective Question Correction","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/subjective-question-correction","siteSpaceId":"sitesp_yCxu9","description":"Detect the errors in subjective question handling. Improve model performance on open-ended or opinion-based tasks.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"iO7GWyEPaZIbLNrDXXE2","title":"Precision@K","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/precision-k","siteSpaceId":"sitesp_yCxu9","description":"Measure how often correct answers appear in top-K results. Track ranking performance for retrieval-augmented systems.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"Pb9J5NdjmUWwXKLj3Gni","title":"Chunk Relevance","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/chunk-relevance","siteSpaceId":"sitesp_yCxu9","description":"Evaluate whether retrieved text chunks are relevant. Identify irrelevant retrievals and refine data pipelines.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"lMKlbATn0aSigIMpOndU","title":"Entity Co-occurrence","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/entity-co-occurrence","siteSpaceId":"sitesp_yCxu9","description":"Test how accurately LLMs connect related entities. Spot gaps in recognition and strengthen relationship extraction.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"9x8nJZVlvonz4q4YoRVh","title":"Fact Entropy","pathname":"/ragaai-catalyst/ragaai-metric-library/information-extraction/fact-entropy","siteSpaceId":"sitesp_yCxu9","description":"Measure uncertainty in LLM facts. Use entropy scoring to identify unreliable answers and boost accuracy.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Information Extraction"}]},{"id":"3u2gfrIhCv2mXauInXQB","title":"Code Generation","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation","siteSpaceId":"sitesp_yCxu9","description":"Evaluate LLMs on generating executable code. Spot syntax or logic errors and improve development workflows.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"5QVqq2ooIT4wbbLMQT4z","title":"Functional Correctness","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/functional-correctness","siteSpaceId":"sitesp_yCxu9","description":"Test whether generated code runs correctly. Identify functional errors and refine LLM outputs for accuracy.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"jxdrFZFz4ZhDGzk98oUk","title":"ChrF","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/chrf","siteSpaceId":"sitesp_yCxu9","description":"Apply ChrF metrics to measure text similarity. Use it for machine translation and summarization evaluation","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"9XTTdIgmS5gM8VyDN9Lp","title":"Ruby","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/ruby","siteSpaceId":"sitesp_yCxu9","description":"Test LLM outputs with Ruby metrics. Assess token overlap for translation accuracy and language precision.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"PwAH3gID7CBggD3xUQDZ","title":"CodeBLEU","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/codebleu","siteSpaceId":"sitesp_yCxu9","description":"Evaluate LLM-generated code with CodeBLEU. Capture logic, syntax, and structure beyond surface similarity.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"KPSsZM8TVveiqz4xymN9","title":"Robust Pass@k","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/robust-pass-k","siteSpaceId":"sitesp_yCxu9","description":"Measure code generation reliability with Pass@k. Assess how often working solutions appear in multiple attempts.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"9YZXAqdMpM5RtOSCHtak","title":"Robust Drop@k","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/robust-drop-k","siteSpaceId":"sitesp_yCxu9","description":"Track failure rates in code attempts with Drop@k. Identify instability in LLM coding performance.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"l2fxWtgnzgV61ydkMXaU","title":"Pass-Ratio@n","pathname":"/ragaai-catalyst/ragaai-metric-library/code-generation/pass-ratio-n","siteSpaceId":"sitesp_yCxu9","description":"Measure the ratio of successful generations at N tries. Track consistency in LLM code generation outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Code Generation"}]},{"id":"fCj1bVcZLkZRBSVV0HKM","title":"Marketing Content Evaluation","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation","siteSpaceId":"sitesp_yCxu9","description":"Analyze AI-generated marketing content for engagement, clarity, and accuracy. Optimize content impact with metrics.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"AhmigXY9V3rixWQocpEj","title":"Engagement Score","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation/engagement-score","siteSpaceId":"sitesp_yCxu9","description":"Measure how engaging LLM-generated marketing content is. Track reader interest and optimize copy impact.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Marketing Content Evaluation"}]},{"id":"3sasBjJWAkPnUZIR1wOs","title":"Misattribution","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation/misattribution","siteSpaceId":"sitesp_yCxu9","description":"Detect when LLMs misattribute facts in marketing text. Improve reliability and avoid misleading outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Marketing Content Evaluation"}]},{"id":"IHBGZ409bRpvopbAevNI","title":"Readability","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation/readability","siteSpaceId":"sitesp_yCxu9","description":"Assess readability of AI-generated marketing copy. Optimize clarity and user understanding across campaigns.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Marketing Content Evaluation"}]},{"id":"bHADO0oX9P9njqIj8O77","title":"Topic Coverage","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation/topic-coverage","siteSpaceId":"sitesp_yCxu9","description":"Check whether AI-generated marketing text covers required topics. Identify missing content and improve coverage.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Marketing Content Evaluation"}]},{"id":"qKVri9h2KJb6OVhriS07","title":"Fabrication","pathname":"/ragaai-catalyst/ragaai-metric-library/marketing-content-evaluation/fabrication","siteSpaceId":"sitesp_yCxu9","description":"Detect fabricated facts in marketing outputs. Ensure content accuracy and safeguard brand credibility.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Marketing Content Evaluation"}]},{"id":"CZZRcrFlDW2xB6mF0rgA","title":"Learning Management System","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system","siteSpaceId":"sitesp_yCxu9","description":"Evaluate AI in LMS tasks. Measure performance on content creation, quiz generation, and student interaction.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"q9wm3A30XM1kXKkr9sWW","title":"Topic Coverage","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/topic-coverage","siteSpaceId":"sitesp_yCxu9","description":"Analyze how well AI-generated LMS content covers intended topics. Ensure completeness and reliability in educational material.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"MCFwewHxRX3xslq8G2nE","title":"Topic Redundancy","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/topic-redundancy","siteSpaceId":"sitesp_yCxu9","description":"Detect redundancy in AI-generated LMS content. Streamline outputs for efficiency and learner engagement.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"byQ6qG433SNDXcruRev5","title":"Question Redundancy","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/question-redundancy","siteSpaceId":"sitesp_yCxu9","description":"Identify repeated questions in LMS assessments. Refine datasets to reduce duplication and improve learning quality.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"7G8wQOFB5HNvNHM4utro","title":"Answer Correctness","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/answer-correctness","siteSpaceId":"sitesp_yCxu9","description":"Evaluate accuracy of AI-generated answers in LMS. Improve reliability of assessments and learning interactions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"PD3v9nZbkWH9DulPmT9J","title":"Source Citability","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/source-citability","siteSpaceId":"sitesp_yCxu9","description":"Test whether LMS content provides proper sources. Improve citation quality and academic reliability.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"VUYJhR4Aj3aN3qYEPKJu","title":"Difficulty Level","pathname":"/ragaai-catalyst/ragaai-metric-library/learning-management-system/difficulty-level","siteSpaceId":"sitesp_yCxu9","description":"Measure difficulty levels in LMS tasks. Balance AI-generated questions for fairness and learner growth.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Learning Management System"}]},{"id":"WLhEbnQpkEwKAKMd5U4Y","title":"Additional Metrics","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics","siteSpaceId":"sitesp_yCxu9","description":"Explore extra evaluation metrics for LLMs. Learn how advanced checks reveal hidden weaknesses in AI systems.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"fQbXWhxEjgVctpGk944p","title":"Guardrails","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails","siteSpaceId":"sitesp_yCxu9","description":"Assess AI guardrails for enforcing safety. Explore methods to keep outputs aligned with rules and policies.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"}]},{"id":"WpLx5cjfVanOa9ndI897","title":"Anonymize","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/anonymize","siteSpaceId":"sitesp_yCxu9","description":"Use anonymization guardrails to hide sensitive data. Protect privacy while maintaining analysis accuracy.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"MOFfGLXQ87VmdnKAKHEr","title":"Deanonymize","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/deanonymize","siteSpaceId":"sitesp_yCxu9","description":"Detect deanonymization risks in AI outputs. Safeguard data security by controlling re-identification attempts.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"9BJVNaAl5BNuQfaF5sCt","title":"Ban Competitors","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/ban-competitors","siteSpaceId":"sitesp_yCxu9","description":"Apply guardrails to block competitor mentions. Control outputs to align with business or compliance goals.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"JHipvce3PJFKfbFVneGW","title":"Ban Substrings","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/ban-substrings","siteSpaceId":"sitesp_yCxu9","description":"Restrict specific substrings in outputs. Use this guardrail to prevent unsafe or undesired content.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"cXvIphKsOf0oBRCqsIsi","title":"Ban Topics","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/ban-topics","siteSpaceId":"sitesp_yCxu9","description":"Block entire topics in LLM outputs. Control model focus and enforce safety policies.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"Qu9GBSKPHge6fZ08w6eF","title":"Code","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/code","siteSpaceId":"sitesp_yCxu9","description":"Apply code-specific guardrails to monitor outputs. Prevent unsafe, malicious, or irrelevant code generations.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"vdvuffjBxKTt0Uiudi06","title":"Invisible Text","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/invisible-text","siteSpaceId":"sitesp_yCxu9","description":"Detect and prevent invisible or hidden text in outputs. Guard against manipulation or misuse.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"smoeeA1uask04nbJkyJo","title":"Language","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/language","siteSpaceId":"sitesp_yCxu9","description":"Enforce language-specific guardrails. Restrict outputs to desired languages and filter unsafe terms.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"BaatwfmW0MibXZsXQo5Z","title":"Secret","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/secret","siteSpaceId":"sitesp_yCxu9","description":"Safeguard against LLMs leaking secrets. Use this guardrail to prevent exposure of sensitive information.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"GVzG2gGyF92l8h1a7ULF","title":"Sentiment","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/sentiment","siteSpaceId":"sitesp_yCxu9","description":"Track sentiment in LLM outputs. Use guardrails to manage tone and align with audience expectations.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"iVgCk3HdMwdgg6vOa5LC","title":"Factual Consistency","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/factual-consistency","siteSpaceId":"sitesp_yCxu9","description":"Ensure AI outputs stay factually consistent. Detect contradictions and improve trustworthiness.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"6GzyAGpA2AmyAkuhBvGK","title":"Language Same","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/language-same","siteSpaceId":"sitesp_yCxu9","description":"Guardrail ensuring language consistency across responses. Prevent mixed-language outputs and enforce clarity.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"rXGU9HZWMDgoSIcxz9Fy","title":"No Refusal","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/no-refusal","siteSpaceId":"sitesp_yCxu9","description":"Stop models from refusing legitimate tasks. Enforce responsiveness while balancing safety.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"uQJtK7J7mMpQBSmSiPrf","title":"Reading Time","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/reading-time","siteSpaceId":"sitesp_yCxu9","description":"Estimate reading time for LLM outputs. Use this guardrail to optimize user experience and content design.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"EFVv9pFvxMFxkFW16hzY","title":"Sensitive","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/sensitive","siteSpaceId":"sitesp_yCxu9","description":"Flag sensitive content in LLM outputs. Strengthen moderation and ensure compliance with safety standards.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"mIxKAjBJ00ncCpTxXY6L","title":"URL Reachability","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/url-reachability","siteSpaceId":"sitesp_yCxu9","description":"Check if generated URLs are reachable. Ensure AI outputs contain working, valid links.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"jGD4lV1hQPSAyurdw1fr","title":"JSON Verify","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/json-verify","siteSpaceId":"sitesp_yCxu9","description":"Validate AI-generated JSON outputs. Use guardrails to prevent schema errors and broken integrations.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Guardrails"}]},{"id":"A3MZ9QkwSzvaVmGq8W4X","title":"Vulnerability Scanner","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner","siteSpaceId":"sitesp_yCxu9","description":"Detect vulnerabilities in AI outputs. Protect against malicious injections and security risks.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"}]},{"id":"Eo1FOcAIrXfZTgQpp8SP","title":"Bullying","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/bullying","siteSpaceId":"sitesp_yCxu9","description":"Identify bullying or harassment language in outputs. Guard against harmful and unsafe responses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"D3QJt7k91WFzNy8cnWKO","title":"Deadnaming","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/deadnaming","siteSpaceId":"sitesp_yCxu9","description":"Detect instances of deadnaming in AI outputs. Protect inclusivity and ensure respectful language use.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"jCej6FovZtIDZoqxmHbF","title":"SexualContent","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/sexualcontent","siteSpaceId":"sitesp_yCxu9","description":"Block explicit or inappropriate sexual content in outputs. Enforce strict safety standards.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"dIxWLUNlE5vpzB52k4VJ","title":"Sexualisation","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/sexualisation","siteSpaceId":"sitesp_yCxu9","description":"Detect harmful sexualisation in AI responses. Safeguard users and enforce responsible AI usage.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"X4YOEn3HViNpPp7vufFP","title":"SlurUsage","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/slurusage","siteSpaceId":"sitesp_yCxu9","description":"Identify and block slurs in LLM outputs. Ensure respectful, non-discriminatory content generation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"fdkj54rd8YFSmhFGnTER","title":"Profanity","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/profanity","siteSpaceId":"sitesp_yCxu9","description":"Detect and restrict profanity in AI outputs. Enforce brand tone and create safer interactions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"6fwRE4a1Alx0BzFpeC7a","title":"QuackMedicine","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/quackmedicine","siteSpaceId":"sitesp_yCxu9","description":"Spot unverified or dangerous medical claims. Prevent misinformation and enforce healthcare content standards.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"CTpWovxN02Fb6orOzCYP","title":"DAN 11","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-11","siteSpaceId":"sitesp_yCxu9","description":"Detect jailbreak prompts like DAN 11. Use guardrails to block unsafe or manipulative model instructions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"cbDMP3x3D0u87omu0RJx","title":"DAN 10","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-10","siteSpaceId":"sitesp_yCxu9","description":"Block prompt jailbreaks such as DAN 10. Strengthen safeguards and prevent misuse of AI models.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"c9DRrXgu4Ol0zPBPv3GN","title":"DAN 9","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-9","siteSpaceId":"sitesp_yCxu9","description":"Identify DAN 9 jailbreak attempts and reinforce safety by blocking malicious instructions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"XssszOsZiYptgSXTvf4V","title":"DAN 8","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-8","siteSpaceId":"sitesp_yCxu9","description":"Detect older jailbreak prompts like DAN 8. Apply guardrails to stop manipulation of model behavior.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"XLflKwpiZxLfL1WYccwK","title":"DAN 7","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-7","siteSpaceId":"sitesp_yCxu9","description":"Block prompt jailbreak strategies from DAN 7. Ensure AI outputs remain aligned with safety rules.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"XEJioIo0DL7iYZLU1Bjh","title":"DAN 6_2","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-6_2","siteSpaceId":"sitesp_yCxu9","description":"Guard against DAN 6_2 jailbreak attempts. Enforce strict protections to avoid policy bypasses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"F9Gvu5EtGvJhM3jAuHdd","title":"DAN 6_0","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-6_0","siteSpaceId":"sitesp_yCxu9","description":"Identify and restrict DAN 6_0 jailbreak prompts. Prevent unsafe AI responses and misuse.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"TlBSPx8ll803VgvWGW1j","title":"DUDE","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dude","siteSpaceId":"sitesp_yCxu9","description":"Block DUDE jailbreak exploits. Apply safeguards to maintain AI integrity and safe outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"bIkwXvxBzsj3RxC4JK5O","title":"STAN","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/stan","siteSpaceId":"sitesp_yCxu9","description":"Detect STAN jailbreak attempts. Strengthen AI guardrails against manipulation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"IDNG3Rn6tP31SRJHnRjb","title":"DAN_JailBreak","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan_jailbreak","siteSpaceId":"sitesp_yCxu9","description":"Identify and block DAN_JailBreak exploits. Safeguard models from unsafe prompt manipulation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"4uXVuyKIYs6BuwwWpN8O","title":"AntiDAN","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/antidan","siteSpaceId":"sitesp_yCxu9","description":"Use AntiDAN protections to block jailbreaks. Ensure AI safety against adversarial prompt attacks.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"marbG0DF2lfALX4IA8BU","title":"ChatGPT_Developer_Mode_v2","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/chatgpt_developer_mode_v2","siteSpaceId":"sitesp_yCxu9","description":"Detect developer Mode jailbreaks. Guard against unsafe manipulations of ChatGPT-like systems.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"Yz3eoWvWT3WafE1hyaX8","title":"ChatGPT_Developer_Mode_RANTI","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/chatgpt_developer_mode_ranti","siteSpaceId":"sitesp_yCxu9","description":"Spot RANTI jailbreak prompts. Apply strict protections to avoid safety bypasses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"j6o3sjtpJGGkQab9zhHp","title":"ChatGPT_Image_Markdown","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/chatgpt_image_markdown","siteSpaceId":"sitesp_yCxu9","description":"Block unsafe image markdown jailbreak exploits. Enforce content safety in AI image generations.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"YwuPQY0RPqemix48Ttle","title":"Ablation_Dan_11_0","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/ablation_dan_11_0","siteSpaceId":"sitesp_yCxu9","description":"Identify Ablation_Dan_11_0 jailbreak attempts. Guard against malicious prompt bypasses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"d8mueQRTkBEblldrypw6","title":"Anthropomorphisation","pathname":"/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/anthropomorphisation","siteSpaceId":"sitesp_yCxu9","description":"Detect anthropomorphisation in outputs. Keep AI responses objective and avoid misleading human-like traits.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Additional Metrics"},{"label":"Vulnerability Scanner"}]},{"id":"dm9JlkO9TcN76LM8cJlI","title":"Guardrails","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails","siteSpaceId":"sitesp_yCxu9","description":"Explore guardrails for AI outputs. Ensure safe, policy-aligned responses across applications.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"}]},{"id":"yF2nO7XEdRUyBSuDqmgL","title":"Competitor Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/competitor-check","siteSpaceId":"sitesp_yCxu9","description":"Block competitor mentions in outputs. Apply guardrails to align with business strategy and compliance.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"gjJwmEOpf7MlcsRCrPaW","title":"Gibberish Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/gibberish-check","siteSpaceId":"sitesp_yCxu9","description":"Detect nonsensical outputs from LLMs. Apply guardrails to ensure clarity and coherence.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"2KKtNkVM2hIqM2FTIMOC","title":"PII","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/pii","siteSpaceId":"sitesp_yCxu9","description":"Flag and block personal data exposure. Protect privacy in AI-generated outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"RK3QngftupTpPic6VMiK","title":"Regex Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/regex-check","siteSpaceId":"sitesp_yCxu9","description":"Validate outputs using regex guardrails. Catch unsafe, malformed, or non-compliant responses automatically.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"v4DokFJuOqqw69PiGj3o","title":"Response Evaluator","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/response-evaluator","siteSpaceId":"sitesp_yCxu9","description":"Use automated guardrails to evaluate responses. Ensure outputs align with expected standards.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"pJxxWsRndvHcmf1Lfwi3","title":"Toxicity","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/toxicity","siteSpaceId":"sitesp_yCxu9","description":"Detect toxic language in LLM responses. Apply strict guardrails to keep interactions safe.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"Isv5LUaSzqlKxUHuewgh","title":"Unusual Prompt","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/unusual-prompt","siteSpaceId":"sitesp_yCxu9","description":"Flag unusual or suspicious prompts. Guard against adversarial inputs that could compromise AI behavior.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"CuxJpizaaSXYmpFUvZ6b","title":"Ban List","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/ban-list","siteSpaceId":"sitesp_yCxu9","description":"Block specific banned terms or patterns. Use guardrails to control AI output safety and compliance.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"zhcyWnveoq8YOsXFgRbg","title":"Detect Drug","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/detect-drug","siteSpaceId":"sitesp_yCxu9","description":"Identify and block drug-related outputs. Enforce safe and compliant AI usage.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"8sZPEQ9cMRCutb7lV0Ov","title":"Detect Redundancy","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/detect-redundancy","siteSpaceId":"sitesp_yCxu9","description":"Spot redundant outputs in AI responses. Optimize for efficiency and clarity in generated content.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"ku955lkTM3LPlB6iSSyS","title":"Detect Secrets","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/detect-secrets","siteSpaceId":"sitesp_yCxu9","description":"Detect secrets like API keys or passwords in model responses. Prevent accidental exposure with automated guardrails.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"sPxVwfV7uwo4PncuAhOI","title":"Financial Tone Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/financial-tone-check","siteSpaceId":"sitesp_yCxu9","description":"Keep AI-generated financial content professional and compliant. Detect tone mismatches and maintain credibility.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"pPxRkRXouZo5aAjHOECH","title":"Has Url","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/has-url","siteSpaceId":"sitesp_yCxu9","description":"Automatically detect URLs in AI outputs. Ensure link presence is flagged and handled correctly.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"2P9AlQ987I3CpDBONT3S","title":"HTML Sanitisation","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/html-sanitisation","siteSpaceId":"sitesp_yCxu9","description":"Strip unsafe HTML from model responses. Prevent injection risks and ensure content safety.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"hIBD6YPrhLl9sxS5M5b7","title":"Live URL","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/live-url","siteSpaceId":"sitesp_yCxu9","description":"Check if AI-generated URLs are live and valid. Ensure outputs contain only working, trustworthy links.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"j7MBXsA2Ya4tgKaOO0zq","title":"Logic Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/logic-check","siteSpaceId":"sitesp_yCxu9","description":"Identify logical flaws or contradictions in model responses. Improve reliability with automated checks.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"MqfznPNo2RaQmUJ4G4fv","title":"Politeness Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/politeness-check","siteSpaceId":"sitesp_yCxu9","description":"Detect impolite or harsh tones in AI responses. Guarantee positive, user-friendly interactions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"UpTqyEIZfKQpYkJ61o0a","title":"Profanity Check","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/profanity-check","siteSpaceId":"sitesp_yCxu9","description":"Automatically detect and block profanity in LLM responses. Maintain safe and professional content.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"AhzKeOuN1regq3eoPAge","title":"Quote Price","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/quote-price","siteSpaceId":"sitesp_yCxu9","description":"Ensure AI-generated content includes correct, valid price quotes. Improve trust in financial AI outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"itGN6UiO3FitLjQmv2kv","title":"Restrict Topics","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/restrict-topics","siteSpaceId":"sitesp_yCxu9","description":"Enforce topic restrictions in LLM outputs. Keep AI conversations safe, relevant, and policy-aligned.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"Fku7ao88lfcJTTnAQvhD","title":"SQL Predicates Guard","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/sql-predicates-guard","siteSpaceId":"sitesp_yCxu9","description":"Prevent dangerous SQL predicates in AI outputs. Protect databases from risky or malicious statements.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"xkYq2GXM4XKQUi4juiDm","title":"Valid CSV","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-csv","siteSpaceId":"sitesp_yCxu9","description":"Ensure AI outputs produce correct CSV structure. Detect errors before data is processed or used","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"t7fEQ13OJg6m7iBLma8c","title":"Valid JSON","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-json","siteSpaceId":"sitesp_yCxu9","description":"Ensure AI outputs produce correct JSON structure. Detect errors before data is processed or used.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"ycNpRVso6RVrKmHWOFGR","title":"Valid Python","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-python","siteSpaceId":"sitesp_yCxu9","description":"Detect syntax errors in AI-generated Python code. Guarantee safe, executable programming outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"H2Es7mfNMQ2OFDFW7pfZ","title":"Valid Range","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-range","siteSpaceId":"sitesp_yCxu9","description":"Ensure numeric values generated by AI fall within allowed ranges. Keep calculations safe and accurate.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"lPAZ3h95EmBIgIsw9BeG","title":"Valid SQL","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-sql","siteSpaceId":"sitesp_yCxu9","description":"Check correctness of SQL outputs from AI models. Prevent invalid or broken database statements.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"aAD4lwcu7VbCxKG8fzt8","title":"Valid URL","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/valid-url","siteSpaceId":"sitesp_yCxu9","description":"Automatically check if URLs generated by AI are valid. Reduce errors with automated guardrails.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"JhlXICxOMPc01OwnmyeK","title":"Cosine Similarity","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/cosine-similarity","siteSpaceId":"sitesp_yCxu9","description":"Compare AI outputs with reference text using cosine similarity. Improve semantic consistency and accuracy.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"cNSxaXTWfIIZ4iETJuZ7","title":"Honesty Detection","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/honesty-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect dishonest or misleading AI outputs. Improve transparency and build trust in model responses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"jVGAkxiXDQzqmk1acSq6","title":"Toxicity Hate Speech","pathname":"/ragaai-catalyst/ragaai-metric-library/guardrails/toxicity-hate-speech","siteSpaceId":"sitesp_yCxu9","description":"Identify toxic or hateful language in AI outputs. Enforce safe, respectful, and policy-compliant content.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Metric Library"},{"label":"Guardrails"}]},{"id":"T8XhvdaKB5JQjcTUJNgw","title":"Prompt Playground","pathname":"/ragaai-catalyst/prompt-playground","siteSpaceId":"sitesp_yCxu9","description":"Test and refine prompts in a sandbox. Compare outputs, optimize instructions, and improve AI performance.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"x37KJc63EFjA1uPF9kEh","title":"Concepts","pathname":"/ragaai-catalyst/prompt-playground/concepts","siteSpaceId":"sitesp_yCxu9","description":"Explore the key concepts behind prompt experimentation. Understand structure, evaluation, and optimization.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"Cdh4iALLIqKPzJgK9Qmv","title":"Single-Prompt Playground","pathname":"/ragaai-catalyst/prompt-playground/single-prompt-playground","siteSpaceId":"sitesp_yCxu9","description":"Run focused experiments with a single prompt. Evaluate AI outputs and fine-tune responses quickly.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"4JPoQM7fhjeXUC8vlNMb","title":"Multiple Prompt Playground","pathname":"/ragaai-catalyst/prompt-playground/multiple-prompt-playground","siteSpaceId":"sitesp_yCxu9","description":"Test and compare multiple prompts simultaneously. Identify which instructions deliver the best AI results.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"HNDJzlFwpmRnsvRE3sJO","title":"Run Evaluations","pathname":"/ragaai-catalyst/prompt-playground/run-evaluations","siteSpaceId":"sitesp_yCxu9","description":"Execute structured evaluations on prompts. Analyze model behavior and ensure reliable, consistent outputs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"017lfaieaXXzSiHshE05","title":"Using Prompt Slugs with Python SDK","pathname":"/ragaai-catalyst/prompt-playground/using-prompt-slugs-with-python-sdk","siteSpaceId":"sitesp_yCxu9","description":"Use prompt slugs in the Python SDK to run tests programmatically. Streamline AI evaluations at scale.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"UQedN99ly2mDTCys1Xsb","title":"Create with AI using Prompt Wizard","pathname":"/ragaai-catalyst/prompt-playground/create-with-ai-using-prompt-wizard","siteSpaceId":"sitesp_yCxu9","description":"Generate optimized prompts using the AI-powered wizard. Simplify experimentation and boost AI reliability.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"TLUupL7oSI89xH4IsKT9","title":"Prompt Diff View","pathname":"/ragaai-catalyst/prompt-playground/prompt-diff-view","siteSpaceId":"sitesp_yCxu9","description":"Visually compare AI responses to different prompts. Spot differences and refine your best-performing instructions.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Prompt Playground"}]},{"id":"8jbDJh4Zl29WozMcLGlH","title":"Synthetic Data Generation","pathname":"/ragaai-catalyst/synthetic-data-generation","siteSpaceId":"sitesp_yCxu9","description":"Generate synthetic data for training and evaluation. Strengthen LLM testing with diverse, controlled datasets.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"ZC1rEOjqEYwr8LKtKBSz","title":"Gateway","pathname":"/ragaai-catalyst/gateway","siteSpaceId":"sitesp_yCxu9","description":"Use Gateway to integrate your LLMs with Catalyst. Streamline testing, monitoring, and evaluation pipelines.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"upiAqOR6t4c4OGPsMGOa","title":"Quickstart","pathname":"/ragaai-catalyst/gateway/quickstart","siteSpaceId":"sitesp_yCxu9","description":"Follow the quickstart guide to set up Gateway. Link LLMs seamlessly and begin evaluating in minutes.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Gateway"}]},{"id":"y5uVmQzSiCi9BzLVzcai","title":"Guardrails","pathname":"/ragaai-catalyst/guardrails","siteSpaceId":"sitesp_yCxu9","description":"Add safety guardrails to AI outputs. Prevent unsafe, biased, or invalid responses with automated checks.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"sHEy8p0Bz5n6PKCtjMVY","title":"Quickstart","pathname":"/ragaai-catalyst/guardrails/quickstart","siteSpaceId":"sitesp_yCxu9","description":"Learn how to set up guardrails quickly. Protect your AI outputs with instant, reliable safety measures.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Guardrails"}]},{"id":"2h6PSV1sl7IUkD8UhoCv","title":"Python SDK","pathname":"/ragaai-catalyst/guardrails/python-sdk","siteSpaceId":"sitesp_yCxu9","description":"Use the Python SDK to enforce guardrails in your applications. Automate safety checks at runtime.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Guardrails"}]},{"id":"QT5kF4rljnAy8sdzbDBd","title":"RagaAI Whitepapers","pathname":"/ragaai-catalyst/ragaai-whitepapers","siteSpaceId":"sitesp_yCxu9","description":"Access in-depth research and frameworks from RagaAI. Explore evaluation methods and insights for LLM testing.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"t1TdvZqwl8AUa7xbyh65","title":"RagaAI RLEF (RAG LLM Evaluation Framework)","pathname":"/ragaai-catalyst/ragaai-whitepapers/ragaai-rlef-rag-llm-evaluation-framework","siteSpaceId":"sitesp_yCxu9","description":"Discover RagaAI’s RLEF whitepaper. Learn a structured framework for evaluating retrieval-augmented generation systems.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"RagaAI Whitepapers"}]},{"id":"nV0HPgEdBcWb1lB2Qq2G","title":"Agentic Testing","pathname":"/ragaai-catalyst/agentic-testing","siteSpaceId":"sitesp_yCxu9","description":"Test agentic AI workflows for reliability and safety. Explore methods to validate multi-step reasoning and decisions.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"JN9PwI6AcV82By2Djj8I","title":"Quickstart","pathname":"/ragaai-catalyst/agentic-testing/quickstart","siteSpaceId":"sitesp_yCxu9","description":"Get started with agentic testing in minutes. Learn setup steps and quickly evaluate AI agent workflows.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"XQlaKtWdj03vT0jNyGGr","title":"Concepts","pathname":"/ragaai-catalyst/agentic-testing/concepts","siteSpaceId":"sitesp_yCxu9","description":"Understand the key concepts of agentic testing. Explore workflows, trace analysis, and evaluation methods.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"KHHDq02vWUyr5TfkUMqa","title":"Tracing","pathname":"/ragaai-catalyst/agentic-testing/concepts/tracing","siteSpaceId":"sitesp_yCxu9","description":"Track each step in AI agent workflows. Use tracing to debug errors, evaluate reasoning, and improve reliability.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Concepts"}]},{"id":"g7F9CP08stdIyBiwlFus","title":"Langgraph (Agentic Tracing)","pathname":"/ragaai-catalyst/agentic-testing/concepts/tracing/langgraph-agentic-tracing","siteSpaceId":"sitesp_yCxu9","description":"Use Langgraph to visualize agent reasoning. Debug, monitor, and optimize multi-step agentic workflows.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Concepts"},{"label":"Tracing"}]},{"id":"NEJ4VI9C5kxdrIYdSswf","title":"RagaAI Catalyst Tracing Guide for Azure OpenAI Users","pathname":"/ragaai-catalyst/agentic-testing/concepts/tracing/ragaai-catalyst-tracing-guide-for-azure-openai-users","siteSpaceId":"sitesp_yCxu9","description":"Learn how to set up tracing for Azure OpenAI models in RagaAI. Ensure detailed monitoring and evaluation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Concepts"},{"label":"Tracing"}]},{"id":"MQ26EJRLcMkpk1DbKtUn","title":"Dynamic Tracing","pathname":"/ragaai-catalyst/agentic-testing/concepts/dynamic-tracing","siteSpaceId":"sitesp_yCxu9","description":"Monitor AI agent behavior dynamically. Detect errors in real time and optimize workflow execution.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Concepts"}]},{"id":"D3uz2gY4qf908hfMDLTC","title":"Application Workflow","pathname":"/ragaai-catalyst/agentic-testing/concepts/application-workflow","siteSpaceId":"sitesp_yCxu9","description":"Learn how to design and manage agentic application workflows. Improve traceability and reliability in testing.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Concepts"}]},{"id":"Zr9nZxkQXYFkMWPvqNjd","title":"Create New Dataset","pathname":"/ragaai-catalyst/agentic-testing/create-new-dataset","siteSpaceId":"sitesp_yCxu9","description":"Create datasets for testing AI models. Add structured data to evaluate performance across multiple scenarios.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"y4kbPRSP1SzNsJLQ5mlj","title":"Metrics","pathname":"/ragaai-catalyst/agentic-testing/metrics","siteSpaceId":"sitesp_yCxu9","description":"Explore metrics that evaluate AI agents. Track hallucinations, honesty, similarity, and more in workflows.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"Xxu3PUOVMkOLMdZcFHKz","title":"Hallucination","pathname":"/ragaai-catalyst/agentic-testing/metrics/hallucination","siteSpaceId":"sitesp_yCxu9","description":"Spot hallucinations in LLM outputs. Identify when models generate made-up content and apply corrections.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Metrics"}]},{"id":"yonZVzDBb1rjZ1RisJRh","title":"Toxicity","pathname":"/ragaai-catalyst/agentic-testing/metrics/toxicity","siteSpaceId":"sitesp_yCxu9","description":"Identify toxic language in AI outputs. Use toxicity detection to enforce safe and respectful responses.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Metrics"}]},{"id":"5tpm4iGx4GwdSN2hDwHy","title":"Honesty","pathname":"/ragaai-catalyst/agentic-testing/metrics/honesty","siteSpaceId":"sitesp_yCxu9","description":"Detect dishonest or misleading responses. Strengthen AI reliability with honesty evaluation metrics.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Metrics"}]},{"id":"uRTGTmDydyb924maCkDQ","title":"Cosine Similarity","pathname":"/ragaai-catalyst/agentic-testing/metrics/cosine-similarity","siteSpaceId":"sitesp_yCxu9","description":"Measure similarity between generated and reference texts. Improve alignment with semantic scoring.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"},{"label":"Metrics"}]},{"id":"A1yaH7BvaBgqzvfJ9bgc","title":"Compare Traces","pathname":"/ragaai-catalyst/agentic-testing/compare-traces","siteSpaceId":"sitesp_yCxu9","description":"Compare execution traces side by side. Identify issues and refine agent reasoning across tasks.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"i6AKHfaQ5FQUfh4ymBtt","title":"Compare Experiments","pathname":"/ragaai-catalyst/agentic-testing/compare-experiments","siteSpaceId":"sitesp_yCxu9","description":"Analyze experiment results across multiple models. Compare workflows to choose the best-performing system.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"kMBw9UzGPwtXOMk89TJC","title":"Add metrics locally","pathname":"/ragaai-catalyst/agentic-testing/add-metrics-locally","siteSpaceId":"sitesp_yCxu9","description":"Extend Catalyst by adding local metrics. Tailor AI evaluation to your organization’s specific needs.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Agentic Testing"}]},{"id":"2NVvPlEP6FIdEi7TwsNK","title":"Custom Metric","pathname":"/ragaai-catalyst/custom-metric","siteSpaceId":"sitesp_yCxu9","description":"Create custom metrics for specialized AI testing. Build unique evaluation standards beyond defaults.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"PpcoJqSFJVzj3Z8WDeyT","title":"Auto Prompt Optimization","pathname":"/ragaai-catalyst/auto-prompt-optimization","siteSpaceId":"sitesp_yCxu9","description":"Automatically optimize prompts for better results. Enhance AI performance with adaptive tuning.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"5WQMxYI4qYDpgbMSi8YP","title":"Human Feedback & Annotations","pathname":"/ragaai-catalyst/human-feedback-and-annotations","siteSpaceId":"sitesp_yCxu9","description":"Collect human feedback on AI responses. Use annotations to refine and improve model performance.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"G0jewSSOnDNXyzx1s04b","title":"Thumbs Up/Down","pathname":"/ragaai-catalyst/human-feedback-and-annotations/thumbs-up-down","siteSpaceId":"sitesp_yCxu9","description":"Capture quick human feedback with thumbs up/down. Improve models using simple quality ratings.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Human Feedback & Annotations"}]},{"id":"yYnGDpReVzTeLlXH6bby","title":"Add Metric Corrections","pathname":"/ragaai-catalyst/human-feedback-and-annotations/add-metric-corrections","siteSpaceId":"sitesp_yCxu9","description":"Correct metric evaluations manually. Ensure fairness and accuracy in AI testing.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Human Feedback & Annotations"}]},{"id":"ZXFvYlQkegmMC7fzVMqy","title":"Corrections as Few-Shot Examples","pathname":"/ragaai-catalyst/human-feedback-and-annotations/corrections-as-few-shot-examples","siteSpaceId":"sitesp_yCxu9","description":"Apply corrections as few-shot examples. Guide models with feedback-driven improvements.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Human Feedback & Annotations"}]},{"id":"iPrqz5GhSgYHBRRVtN13","title":"Tagging","pathname":"/ragaai-catalyst/human-feedback-and-annotations/tagging","siteSpaceId":"sitesp_yCxu9","description":"Tag AI responses for easy categorization. Streamline analysis with structured annotations.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"Human Feedback & Annotations"}]},{"id":"uKlgJyh0MJcFzX8neokb","title":"On-Premise Deployment","pathname":"/ragaai-catalyst/on-premise-deployment","siteSpaceId":"sitesp_yCxu9","description":"RagaAI Catalyst can be used in both a SaaS mode as well as within the customer's environment (on-prem). This section contains details about hosting RagaAI Catalyst on-prem.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"V81IX9fVB6MW4bav6mHv","title":"Self-Hosting RagaAI Catalyst on Kubernetes","pathname":"/ragaai-catalyst/on-premise-deployment/self-hosting-catalyst-on-k8s","siteSpaceId":"sitesp_yCxu9","description":"Learn how to self-host Catalyst on Kubernetes. Scale AI testing with secure, containerized deployment.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"}]},{"id":"f9vQ0H82xy3C7krDOJbg","title":"Enterprise Deployment Guide for AWS","pathname":"/ragaai-catalyst/on-premise-deployment/on-premise-deployment-for-aws","siteSpaceId":"sitesp_yCxu9","description":"Deploy Catalyst seamlessly on AWS. Follow enterprise-ready steps for secure and scalable setup.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"}]},{"id":"2zhuew7iJY0VlR3D9r2t","title":"Enterprise Deployment Guide for Azure","pathname":"/ragaai-catalyst/on-premise-deployment/on-premise-deployment-for-azure","siteSpaceId":"sitesp_yCxu9","description":"Learn how to deploy Catalyst on Azure. Follow structured steps for enterprise-ready AI testing.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"}]},{"id":"71eFHZF9PF1svbsKImyD","title":"Enterprise Deployment Guide for GCP","pathname":"/ragaai-catalyst/on-premise-deployment/on-premise-deployment-for-gcp","siteSpaceId":"sitesp_yCxu9","description":"Deploy Catalyst on Google Cloud. Follow enterprise instructions for scalable AI evaluation.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"}]},{"id":"XPwKUOFZ0Rq1Rsf3bSsT","title":"Evaluation Deployment Guide","pathname":"/ragaai-catalyst/on-premise-deployment/raga-catalyst-deployment-guide","siteSpaceId":"sitesp_yCxu9","description":"Step-by-step guide for deploying Catalyst evaluation. Ensure smooth setup for AI testing.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"}]},{"id":"Mby2dKvbgpci3TrxZ9XP","title":"Evaluation Maintenance Guide","pathname":"/ragaai-catalyst/on-premise-deployment/raga-catalyst-deployment-guide/evaluation-maintenance-guide","siteSpaceId":"sitesp_yCxu9","description":"Maintain Catalyst deployments effectively. Follow best practices for stability and uptime.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"On-Premise Deployment"},{"label":"Evaluation Deployment Guide"}]},{"id":"SCWDBN0z4uxzOvAI48TH","title":"Fine Tuning (OpenAI)","pathname":"/ragaai-catalyst/fine-tuning","siteSpaceId":"sitesp_yCxu9","description":"Learn how to fine-tune OpenAI models within Catalyst. Improve accuracy with custom training data.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"0Jkh4gQTOE8SQ0tbQ00G","title":"Integration","pathname":"/ragaai-catalyst/integration","siteSpaceId":"sitesp_yCxu9","description":"Integrate Catalyst seamlessly into your workflows. Connect tools, data, and models in one platform.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"BMTSF8QKt96m6BJyEBHo","title":"SDK Release Notes","pathname":"/ragaai-catalyst/sdk-release-notes","siteSpaceId":"sitesp_yCxu9","description":"Stay updated with the latest SDK changes. Review features, fixes, and improvements for Catalyst.","breadcrumbs":[{"label":"RagaAI Catalyst"}]},{"id":"MQievXQyWEoAMH7O3HH9","title":"ragaai-catalyst 2.1.7","pathname":"/ragaai-catalyst/sdk-release-notes/ragaai-catalyst-2.1.7","siteSpaceId":"sitesp_yCxu9","description":"Explore updates in Catalyst v2.1.7. Review new features, enhancements, and bug fixes.","breadcrumbs":[{"label":"RagaAI Catalyst"},{"label":"SDK Release Notes"}]},{"id":"DR7ry4q63BfQP1csZ5bq","title":"RagaAI Prism","pathname":"/ragaai-prism","siteSpaceId":"sitesp_yCxu9","description":"This page provides a high level introduction to the RagaAI Prism - An end to end testing platform for DiscriminativeAI models - Computer Vision, NLP and Tabular Data."},{"id":"rll7dO6QJAhX2u4va7I9","title":"Quickstart","pathname":"/ragaai-prism/quickstart","siteSpaceId":"sitesp_yCxu9","description":"Get started with Prism quickly. Follow steps to begin testing and evaluating AI models in minutes.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"HvTfNe5nPA9GAqzPNEG7","title":"Sandbox Guide","pathname":"/ragaai-prism/sandbox-guide","siteSpaceId":"sitesp_yCxu9","description":"Learn how to use Prism sandbox. Run structured experiments and validate model performance.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"kju0FP4r0m6PbK7yFZTh","title":"Object Detection","pathname":"/ragaai-prism/sandbox-guide/object-detection","siteSpaceId":"sitesp_yCxu9","description":"Evaluate object detection models with Prism. Identify errors, drift, and model weaknesses.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"5ShkNiuvqQQD72VPomuN","title":"LLM Summarization","pathname":"/ragaai-prism/sandbox-guide/llm-summarization","siteSpaceId":"sitesp_yCxu9","description":"Assess summarization performance of LLMs. Detect fluency, coherence, and coverage issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"s62EFhCrzGa60Gmc2Kxz","title":"Semantic Segmentation","pathname":"/ragaai-prism/sandbox-guide/semantic-segmentation","siteSpaceId":"sitesp_yCxu9","description":"Test semantic segmentation models in Prism. Spot class imbalance and analyze labeling quality.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"hbYQwMeDoW5qdBwnq6M5","title":"Tabular Data","pathname":"/ragaai-prism/sandbox-guide/tabular-data","siteSpaceId":"sitesp_yCxu9","description":"Evaluate models on tabular data. Detect drift, imbalance, and errors in structured predictions.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"OEC4qrQzUH3DxOGF2UgB","title":"Super Resolution","pathname":"/ragaai-prism/sandbox-guide/super-resolution","siteSpaceId":"sitesp_yCxu9","description":"Assess AI models for image super resolution. Identify drift, errors, and resolution quality.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"6UKT5LhOEvg0XIItEQDL","title":"OCR","pathname":"/ragaai-prism/sandbox-guide/ocr","siteSpaceId":"sitesp_yCxu9","description":"Test OCR models in Prism. Detect missing values, outliers, and errors in extracted text.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"m4cUN4wW2p1bKOYQ5Fmv","title":"Image Classification","pathname":"/ragaai-prism/sandbox-guide/image-classification","siteSpaceId":"sitesp_yCxu9","description":"Evaluate classification models. Detect drift, imbalance, and labeling issues in outputs.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"FcMdsHHAJy1I6k3ehUtF","title":"Event Detection","pathname":"/ragaai-prism/sandbox-guide/event-detection","siteSpaceId":"sitesp_yCxu9","description":"Test event detection models for accuracy. Identify drift, bias, and classification errors.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Sandbox Guide"}]},{"id":"0Xh0CyTyvOTeZgcSG2RO","title":"Test Inventory","pathname":"/ragaai-prism/test-inventory","siteSpaceId":"sitesp_yCxu9","description":"Access a library of tests for AI models. Use Prism’s inventory to validate diverse model behaviors.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"N7vlOqED9F51z8LdMM3p","title":"Object Detection","pathname":"/ragaai-prism/test-inventory/object-detection","siteSpaceId":"sitesp_yCxu9","description":"Test object detection models thoroughly. Identify weaknesses, drift, and labeling issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"kwvDRHlcos28PDxXOCgp","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/object-detection/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Analyze failure modes in object detection. Spot recurring errors and refine models.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"00hlhojDZ5Fcygntcy1b","title":"Model Comparison Test","pathname":"/ragaai-prism/test-inventory/object-detection/model-comparison-test","siteSpaceId":"sitesp_yCxu9","description":"Compare object detection models side by side. Benchmark performance to find the most reliable.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"7F6HA9WFWWVuZlLcOLae","title":"Drift Detection","pathname":"/ragaai-prism/test-inventory/object-detection/drift-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect drift in object detection models. Track shifts in data and retrain before accuracy drops.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"whFLwIfsvBxhsZHTNzxG","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/object-detection/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"Identify anomalies in object detection outputs. Catch unusual predictions early.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"nXYQSiEinz7huIMKlRIt","title":"Data Leakage Test","pathname":"/ragaai-prism/test-inventory/object-detection/data-leakage-test","siteSpaceId":"sitesp_yCxu9","description":"Test datasets for leakage in object detection. Prevent training-test contamination.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"yvSy3sKFHHSHn6bcJdzl","title":"Labelling Quality Test","pathname":"/ragaai-prism/test-inventory/object-detection/labelling-quality-test","siteSpaceId":"sitesp_yCxu9","description":"Check annotation accuracy in datasets. Improve object detection results with cleaner labels.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"KaEkHVKRZDnrCz2AQjkM","title":"Scenario Imbalance","pathname":"/ragaai-prism/test-inventory/object-detection/scenario-imbalance","siteSpaceId":"sitesp_yCxu9","description":"Identify imbalanced scenarios in training data. Improve fairness and robustness.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"sjCFSOZsO8AzfXGgzuK6","title":"Class Imbalance","pathname":"/ragaai-prism/test-inventory/object-detection/class-imbalance","siteSpaceId":"sitesp_yCxu9","description":"Detect class imbalance issues. Improve model performance with balanced data.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"Rs2FPMRROAbDexs5Tqe3","title":"Active Learning","pathname":"/ragaai-prism/test-inventory/object-detection/active-learning","siteSpaceId":"sitesp_yCxu9","description":"Use active learning strategies to improve detection models. Retrain with the most informative samples.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"lJKG3s0jDcSinMZV0XyS","title":"Image Property Drift Detection","pathname":"/ragaai-prism/test-inventory/object-detection/image-property-drift-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect changes in image properties that affect detection. Keep models robust against drift.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Object Detection"}]},{"id":"i8QWwnZMqWl2KQTXsF4q","title":"Large Language Model (LLM)","pathname":"/ragaai-prism/test-inventory/large-language-model-llm","siteSpaceId":"sitesp_yCxu9","description":"Test LLMs with Prism. Evaluate reasoning, hallucination, and output reliability.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"3xc5BXXxhXU5EirDDYlc","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/large-language-model-llm/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Analyze where LLMs fail. Spot reasoning errors, hallucinations, and inconsistencies.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Large Language Model (LLM)"}]},{"id":"OsI7JRUvohXZ8W7fLI8A","title":"Semantic Segmentation","pathname":"/ragaai-prism/test-inventory/semantic-segmentation","siteSpaceId":"sitesp_yCxu9","description":"Test semantic segmentation models for robustness. Detect errors, drift, and imbalance issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"f7e9h3enklisuTNzE0tU","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Analyze segmentation model errors. Identify recurring weaknesses to improve performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"snCDM4nZnOvyrF7tRZVd","title":"Labelling Quality Test","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/labelling-quality-test","siteSpaceId":"sitesp_yCxu9","description":"Validate labeling quality in segmentation datasets. Improve accuracy with clean annotations.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"v8foW8s8wGzHOOnAyYgG","title":"Active Learning","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/active-learning","siteSpaceId":"sitesp_yCxu9","description":"Apply active learning to boost segmentation accuracy. Retrain with most informative samples.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"FCkYUf6R1spAqCQGCLcX","title":"Drift Detection","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/drift-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect drift in semantic segmentation inputs. Catch data shifts early to preserve accuracy.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"WEQzVd8zFJueS772lwjB","title":"Class Imbalance","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/class-imbalance","siteSpaceId":"sitesp_yCxu9","description":"Identify and correct class imbalance. Improve fairness and accuracy of segmentation models.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"UwbyjnQmEECJo9vNuIla","title":"Scenario Imbalance","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/scenario-imbalance","siteSpaceId":"sitesp_yCxu9","description":"Spot imbalance in dataset scenarios. Ensure diverse coverage for better segmentation outcomes.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"wcPBsv7xWOsUSWN41HxP","title":"Data Leakage Test","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/data-leakage-test","siteSpaceId":"sitesp_yCxu9","description":"Test for data leakage in segmentation tasks. Ensure clean splits between training and evaluation.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"HrjcK24WZDUz2x5LXVCX","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"Identify unusual segmentation outputs. Detect anomalies before they harm performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"mxx2yVgPShITf9xGGprm","title":"Label Drift","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/label-drift","siteSpaceId":"sitesp_yCxu9","description":"Detect shifts in labels over time. Ensure annotation consistency in evolving datasets.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"zCG98UiC9M8ZiEima8Sl","title":"Semantic Similarity","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/semantic-similarity","siteSpaceId":"sitesp_yCxu9","description":"Evaluate similarity between segmentation outputs. Improve consistency with semantic checks.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"qHaaSXWWbW0BGTVfiaQ4","title":"Near Duplicates Detection","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/near-duplicates-detection","siteSpaceId":"sitesp_yCxu9","description":"Identify near-duplicate samples in datasets. Improve segmentation training with clean, diverse data.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"XldqmxPocjeEY9MhVYsm","title":"Cluster Imbalance Test","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/cluster-imbalance-test","siteSpaceId":"sitesp_yCxu9","description":"Detect imbalance across data clusters. Ensure segmentation models train on fair, balanced distributions.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"N6oDAufEDTa8hAUsdj2G","title":"Image Property Drift Detection","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/image-property-drift-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect changes in image properties that affect segmentation accuracy. Keep models robust with drift checks..","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"eCvrddikDR9RVruIVSWp","title":"Spatio-Temporal Drift Detection","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/spatio-temporal-drift-detection","siteSpaceId":"sitesp_yCxu9","description":"Detect drift across space and time dimensions. Maintain segmentation model reliability with ongoing checks.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"z2Z2n1rM7hYnnmy8ixyC","title":"Spatio-Temporal Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/semantic-segmentation/spatio-temporal-failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"The Spatio-Temporal Failure Mode Analysis Test is designed to analyze the model's performance on the spatio-temporal dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Semantic Segmentation"}]},{"id":"pWGN06yRw3Kqvfd6jKaY","title":"Tabular Data","pathname":"/ragaai-prism/test-inventory/tabular-data","siteSpaceId":"sitesp_yCxu9","description":"RagaAI offers robust testing support across a spectrum of machine learning models and AI applications.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"AJCiIhnsI7SvQsXyaLu8","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/tabular-data/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Failure Mode Analysis is a test that allows users to deeply analyse their model's performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Tabular Data"}]},{"id":"EvbSjIjQmOqtTRf1bu3F","title":"Instance Segmentation","pathname":"/ragaai-prism/test-inventory/instance-segmentation","siteSpaceId":"sitesp_yCxu9","description":"Test instance segmentation models. Detect imbalance, drift, and labeling quality issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"UNLLK8OhoLWpuo535Tly","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/instance-segmentation/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Failure Mode Analysis is a test that allows users to deeply analyse their model's performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"2mXWRBAuBtq60YoR3kyg","title":"Labelling Quality Test","pathname":"/ragaai-prism/test-inventory/instance-segmentation/labelling-quality-test","siteSpaceId":"sitesp_yCxu9","description":"Check annotation accuracy in segmentation datasets. Improve outcomes with reliable labeling.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"mUYCNZnYRUPSWrxxjbyO","title":"Drift Detection","pathname":"/ragaai-prism/test-inventory/instance-segmentation/drift-detection","siteSpaceId":"sitesp_yCxu9","description":"The Drift Detection Test allows users to identify shifts between training and field/test datasets","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"6EP0qFoX1ibmSHNHCsVF","title":"Class Imbalance","pathname":"/ragaai-prism/test-inventory/instance-segmentation/class-imbalance","siteSpaceId":"sitesp_yCxu9","description":"Identify class imbalance issues in datasets. Train robust instance segmentation models with fair data.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"8zt39AmIvNisR78nF2OQ","title":"Scenario Imbalance","pathname":"/ragaai-prism/test-inventory/instance-segmentation/scenario-imbalance","siteSpaceId":"sitesp_yCxu9","description":"The Scenario Imbalance Test evaluates the distribution of scenarios or contexts within a dataset, providing insights into potential imbalances that may affect model performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"yIdbBrJ0zzO0dOvMqCfs","title":"Label Drift","pathname":"/ragaai-prism/test-inventory/instance-segmentation/label-drift","siteSpaceId":"sitesp_yCxu9","description":"Monitor changes in dataset labels over time. Prevent quality drops in segmentation training.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"9HqFlKQVaNQIqsT2P2QZ","title":"Data Leakage Test","pathname":"/ragaai-prism/test-inventory/instance-segmentation/data-leakage-test","siteSpaceId":"sitesp_yCxu9","description":"Detect leakage between training and test data. Improve model fairness and trustworthiness.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"RpLCDqdajzSrxWgNg3RK","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/instance-segmentation/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"The Outlier Detection Test in RagaAI is crucial for identifying anomalies in your dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"DXAI0Ou9bQpTQkzxa9Ub","title":"Active Learning","pathname":"/ragaai-prism/test-inventory/instance-segmentation/active-learning","siteSpaceId":"sitesp_yCxu9","description":"The Active Learning Test in RagaAI optimises dataset by selecting the most representative data points within a specified budget.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"IUvTzXL7KxwcxE1q3tVW","title":"Near Duplicates Detection","pathname":"/ragaai-prism/test-inventory/instance-segmentation/near-duplicates-detection","siteSpaceId":"sitesp_yCxu9","description":"The Near Duplicate Detection Test in RagaAI is designed to identify both exact and near duplicates within your image dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Instance Segmentation"}]},{"id":"PWBtrXFse1x21Hoa6wlG","title":"Super Resolution","pathname":"/ragaai-prism/test-inventory/super-resolution","siteSpaceId":"sitesp_yCxu9","description":"Evaluate super resolution models. Detect drift, anomalies, and output quality issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"9cBLyiH9rII8JrFPfJ4F","title":"Semantic Similarity","pathname":"/ragaai-prism/test-inventory/super-resolution/semantic-similarity","siteSpaceId":"sitesp_yCxu9","description":"The Semantic Similarity Test in RagaAI assesses the likeness between high-resolution (HR) and low-resolution (LR) images.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Super Resolution"}]},{"id":"XCGAWW6eGryBxuycrbpL","title":"Active Learning","pathname":"/ragaai-prism/test-inventory/super-resolution/active-learning","siteSpaceId":"sitesp_yCxu9","description":"The Active Learning Test in RagaAI optimises dataset by selecting the most representative data points within a specified budget.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Super Resolution"}]},{"id":"7yObYBWazdLRmz5P7bBH","title":"Near Duplicates Detection","pathname":"/ragaai-prism/test-inventory/super-resolution/near-duplicates-detection","siteSpaceId":"sitesp_yCxu9","description":"The Near Duplicate Detection Test in RagaAI is designed to identify both exact and near duplicates within your image dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Super Resolution"}]},{"id":"OiR0ipwmeHSAzgor1x4k","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/super-resolution/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"The Outlier Detection Test in RagaAI is crucial for identifying anomalies in low-resolution and high-resolution datasets, separately.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Super Resolution"}]},{"id":"x46gJbJU5tPSuDLtgcMe","title":"OCR","pathname":"/ragaai-prism/test-inventory/ocr","siteSpaceId":"sitesp_yCxu9","description":"RagaAI offers robust testing support across a spectrum of machine learning models and AI applications.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"YxwbYE66bSJ5NVrDLGFe","title":"Missing Value Test","pathname":"/ragaai-prism/test-inventory/ocr/missing-value-test","siteSpaceId":"sitesp_yCxu9","description":"The Missing Values Test enables you to identify data points with missing bounding boxes. Use filters to filter by class or by test results to directly look at data points of interest.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"OCR"}]},{"id":"yopqEZvGeraqWOFkRVfa","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/ocr/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"The Outlier Detection Test in RagaAI is crucial for identifying anomalies in low-resolution and high-resolution datasets, separately.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"OCR"}]},{"id":"BwDImxlZ4Dc8FytGXV52","title":"Image Classification","pathname":"/ragaai-prism/test-inventory/image-classification","siteSpaceId":"sitesp_yCxu9","description":"Test image classification models. Detect class imbalance, drift, and labeling quality issues.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"xqJdJLgmYjBaEePxmwXe","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/image-classification/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Analyze common errors in classification tasks. Refine training and improve model accuracy.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"8iwGaevJyGIlAdAvviXf","title":"Labelling Quality Test","pathname":"/ragaai-prism/test-inventory/image-classification/labelling-quality-test","siteSpaceId":"sitesp_yCxu9","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"wxwVRKL8hGMNvyulMXaF","title":"Class Imbalance","pathname":"/ragaai-prism/test-inventory/image-classification/class-imbalance","siteSpaceId":"sitesp_yCxu9","description":"The Class Imbalance Test is designed to assess the distribution of classes within a dataset, particularly in the context of machine learning tasks like object detection.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"VIPWMUYZ3inWZ2j8GasJ","title":"Drift Detection","pathname":"/ragaai-prism/test-inventory/image-classification/drift-detection","siteSpaceId":"sitesp_yCxu9","description":"The Drift Detection Test allows users to identify shifts between training and field/test datasets","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"ibED1uL86RfCDs76abZj","title":"Near Duplicates Test","pathname":"/ragaai-prism/test-inventory/image-classification/near-duplicates-test","siteSpaceId":"sitesp_yCxu9","description":"The Near Duplicate Detection Test in RagaAI is designed to identify both exact and near duplicates within your image dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"S7c7jfS8oyrGTKD4F0vW","title":"Data Leakage Test","pathname":"/ragaai-prism/test-inventory/image-classification/data-leakage-test","siteSpaceId":"sitesp_yCxu9","description":"The Data Leakage Test results provide insights into the presence of data leakage from the training dataset to the test/validation dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"OPQ5NMaxw27dj1DrlyQ3","title":"Outlier Detection","pathname":"/ragaai-prism/test-inventory/image-classification/outlier-detection","siteSpaceId":"sitesp_yCxu9","description":"The Outlier Detection Test in RagaAI is crucial for identifying anomalies in your dataset.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"LhIYXMx6rL1eAaaKqM9t","title":"Active Learning","pathname":"/ragaai-prism/test-inventory/image-classification/active-learning","siteSpaceId":"sitesp_yCxu9","description":"The Active Learning Test in RagaAI optimises dataset by selecting the most representative data points within a specified budget.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"K7deFx2CXFGaiUcxb6J7","title":"Image Property Drift Detection","pathname":"/ragaai-prism/test-inventory/image-classification/image-property-drift-detection","siteSpaceId":"sitesp_yCxu9","description":"The Image Property Drift Detection Test is designed to monitor the shifts in individual image properties over time.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Image Classification"}]},{"id":"SjyEZ9ycG3Uq5EqNXRMg","title":"Event Detection","pathname":"/ragaai-prism/test-inventory/event-detection","siteSpaceId":"sitesp_yCxu9","description":"RagaAI offers robust testing support across a spectrum of machine learning models and AI applications.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"}]},{"id":"BGXnixNgYRnQ965v8TVb","title":"Failure Mode Analysis","pathname":"/ragaai-prism/test-inventory/event-detection/failure-mode-analysis","siteSpaceId":"sitesp_yCxu9","description":"Failure Mode Analysis is a test that allows users to deeply analyse the pipeline performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Event Detection"}]},{"id":"FdOkAimmr5rMUwHGzcBF","title":"A/B Test","pathname":"/ragaai-prism/test-inventory/event-detection/a-b-test","siteSpaceId":"sitesp_yCxu9","description":"A/B Test is a systematic method to compare two or more variations of a pipeline.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Test Inventory"},{"label":"Event Detection"}]},{"id":"Emx1RvVwxLeKaAPldksZ","title":"Metric Glossary","pathname":"/ragaai-prism/metric-glossary","siteSpaceId":"sitesp_yCxu9","description":"Quick definitions and explanations of the various metrics used within the RagaAI Testing Platform","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"XSscksiFaFbZgKJ9pFOA","title":"Upload custom model","pathname":"/ragaai-prism/upload-custom-model","siteSpaceId":"sitesp_yCxu9","description":"Upload your own models into Prism. Run evaluations and validate performance on your datasets.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"zMTLhjNpmAhQuDQUhHaq","title":"Event Detection","pathname":"/ragaai-prism/event-detection","siteSpaceId":"sitesp_yCxu9","description":"Evaluate event detection with Prism tools. Identify drift, anomalies, and misclassifications.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"YeZ6IBgldRb7eLhtn6Jb","title":"Upload Model","pathname":"/ragaai-prism/event-detection/upload-model","siteSpaceId":"sitesp_yCxu9","description":"Upload event detection models into Prism. Start evaluations and monitor real-world performance.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Event Detection"}]},{"id":"CGGltONaueTKEyGCaB4M","title":"Generate Inference","pathname":"/ragaai-prism/event-detection/generate-inference","siteSpaceId":"sitesp_yCxu9","description":"Run inferences with event detection models. Evaluate predictions and test outputs instantly.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Event Detection"}]},{"id":"x1wzwFyz6wJhGyHbGrvr","title":"Run tests","pathname":"/ragaai-prism/event-detection/run-tests","siteSpaceId":"sitesp_yCxu9","description":"Execute structured tests on event detection models. Track performance across conditions.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"Event Detection"}]},{"id":"tvr6oOa2pj7wnn2N9RIE","title":"On-Premise Deployment","pathname":"/ragaai-prism/on-premise-deployment","siteSpaceId":"sitesp_yCxu9","description":"RagaAI Prism can be used in both a SaaS mode as well as within the customer's environment (on-prem). This section contains details about hosting RagaAI Prism on-prem.","breadcrumbs":[{"label":"RagaAI Prism"}]},{"id":"8leAJdtntYERBxDuTL5i","title":"Enterprise Deployment Guide for AWS","pathname":"/ragaai-prism/on-premise-deployment/on-premise-deployment-for-aws","siteSpaceId":"sitesp_yCxu9","description":"Learn how to deploy Prism on AWS. Follow enterprise-ready instructions for secure AI testing.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"On-Premise Deployment"}]},{"id":"9oyfAIYbROBzCnnQWWO3","title":"Enterprise Deployment Guide for Azure","pathname":"/ragaai-prism/on-premise-deployment/on-premise-deployment-for-azure","siteSpaceId":"sitesp_yCxu9","description":"Deploy Prism seamlessly on Azure. Follow best practices for scalable, compliant AI evaluation.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"On-Premise Deployment"}]},{"id":"QSGW49lihMJfCiy67Zxu","title":"Enterprise Deployment Guide for GCP","pathname":"/ragaai-prism/on-premise-deployment/on-premise-deployment-for-gcp","siteSpaceId":"sitesp_yCxu9","description":"Deploy Prism on Google Cloud. Ensure secure, enterprise-ready setup for AI testing.","breadcrumbs":[{"label":"RagaAI Prism"},{"label":"On-Premise Deployment"}]},{"id":"1WqGYmU6Za8jSuXfxqYI","title":"Support","pathname":"/support","siteSpaceId":"sitesp_yCxu9","description":"Access RagaAI support resources. Get help with Catalyst, Prism, integrations, and deployment."}]}