# Guardrails - [Competitor Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/competitor-check.md): Block competitor mentions in outputs. Apply guardrails to align with business strategy and compliance. - [Gibberish Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/gibberish-check.md): Detect nonsensical outputs from LLMs. Apply guardrails to ensure clarity and coherence. - [PII](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/pii.md): Flag and block personal data exposure. Protect privacy in AI-generated outputs. - [Regex Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/regex-check.md): Validate outputs using regex guardrails. Catch unsafe, malformed, or non-compliant responses automatically. - [Response Evaluator](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/response-evaluator.md): Use automated guardrails to evaluate responses. Ensure outputs align with expected standards. - [Toxicity](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/toxicity.md): Detect toxic language in LLM responses. Apply strict guardrails to keep interactions safe. - [Unusual Prompt](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/unusual-prompt.md): Flag unusual or suspicious prompts. Guard against adversarial inputs that could compromise AI behavior. - [Ban List](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/ban-list.md): Block specific banned terms or patterns. Use guardrails to control AI output safety and compliance. - [Detect Drug](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/detect-drug.md): Identify and block drug-related outputs. Enforce safe and compliant AI usage. - [Detect Redundancy](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/detect-redundancy.md): Spot redundant outputs in AI responses. Optimize for efficiency and clarity in generated content. - [Detect Secrets](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/detect-secrets.md): Detect secrets like API keys or passwords in model responses. Prevent accidental exposure with automated guardrails. - [Financial Tone Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/financial-tone-check.md): Keep AI-generated financial content professional and compliant. Detect tone mismatches and maintain credibility. - [Has Url](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/has-url.md): Automatically detect URLs in AI outputs. Ensure link presence is flagged and handled correctly. - [HTML Sanitisation](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/html-sanitisation.md): Strip unsafe HTML from model responses. Prevent injection risks and ensure content safety. - [Live URL](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/live-url.md): Check if AI-generated URLs are live and valid. Ensure outputs contain only working, trustworthy links. - [Logic Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/logic-check.md): Identify logical flaws or contradictions in model responses. Improve reliability with automated checks. - [Politeness Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/politeness-check.md): Detect impolite or harsh tones in AI responses. Guarantee positive, user-friendly interactions. - [Profanity Check](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/profanity-check.md): Automatically detect and block profanity in LLM responses. Maintain safe and professional content. - [Quote Price](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/quote-price.md): Ensure AI-generated content includes correct, valid price quotes. Improve trust in financial AI outputs. - [Restrict Topics](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/restrict-topics.md): Enforce topic restrictions in LLM outputs. Keep AI conversations safe, relevant, and policy-aligned. - [SQL Predicates Guard](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/sql-predicates-guard.md): Prevent dangerous SQL predicates in AI outputs. Protect databases from risky or malicious statements. - [Valid CSV](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-csv.md): Ensure AI outputs produce correct CSV structure. Detect errors before data is processed or used - [Valid JSON](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-json.md): Ensure AI outputs produce correct JSON structure. Detect errors before data is processed or used. - [Valid Python](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-python.md): Detect syntax errors in AI-generated Python code. Guarantee safe, executable programming outputs. - [Valid Range](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-range.md): Ensure numeric values generated by AI fall within allowed ranges. Keep calculations safe and accurate. - [Valid SQL](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-sql.md): Check correctness of SQL outputs from AI models. Prevent invalid or broken database statements. - [Valid URL](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/valid-url.md): Automatically check if URLs generated by AI are valid. Reduce errors with automated guardrails. - [Cosine Similarity](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/cosine-similarity.md): Compare AI outputs with reference text using cosine similarity. Improve semantic consistency and accuracy. - [Honesty Detection](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/honesty-detection.md): Detect dishonest or misleading AI outputs. Improve transparency and build trust in model responses. - [Toxicity Hate Speech](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/toxicity-hate-speech.md): Identify toxic or hateful language in AI outputs. Enforce safe, respectful, and policy-compliant content. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.