LogoLogo
Slack CommunityCatalyst Login
  • Welcome
  • RagaAI Catalyst
    • User Quickstart
    • Concepts
      • Configure Your API Keys
      • Supported LLMs
        • OpenAI
        • Gemini
        • Azure
        • AWS Bedrock
        • ANTHROPIC
      • Catalyst Access/Secret Keys
      • Enable Custom Gateway
      • Uploading Data
        • Create new project
        • RAG Datset
        • Chat Dataset
          • Prompt Format
        • Logging traces (LlamaIndex, Langchain)
        • Trace Masking Functions
        • Trace Level Metadata
        • Correlating Traces with External IDs
        • Add Dataset
      • Running RagaAI Evals
        • Executing Evaluations
        • Compare Datasets
      • Analysis
      • Embeddings
    • RagaAI Metric Library
      • RAG Metrics
        • Hallucination
        • Faithfulness
        • Response Correctness
        • Response Completeness
        • False Refusal
        • Context Relevancy
        • Context Precision
        • Context Recall
        • PII Detection
        • Toxicity
      • Chat Metrics
        • Agent Quality
        • Instruction Adherence
        • User Chat Quality
      • Text-to-SQL
        • SQL Response Correctness
        • SQL Prompt Ambiguity
        • SQL Context Ambiguity
        • SQL Context Sufficiency
        • SQL Prompt Injection
      • Text Summarization
        • Summary Consistency
        • Summary Relevance
        • Summary Fluency
        • Summary Coherence
        • SummaC
        • QAG Score
        • ROUGE
        • BLEU
        • METEOR
        • BERTScore
      • Information Extraction
        • MINEA
        • Subjective Question Correction
        • Precision@K
        • Chunk Relevance
        • Entity Co-occurrence
        • Fact Entropy
      • Code Generation
        • Functional Correctness
        • ChrF
        • Ruby
        • CodeBLEU
        • Robust Pass@k
        • Robust Drop@k
        • Pass-Ratio@n
      • Marketing Content Evaluation
        • Engagement Score
        • Misattribution
        • Readability
        • Topic Coverage
        • Fabrication
      • Learning Management System
        • Topic Coverage
        • Topic Redundancy
        • Question Redundancy
        • Answer Correctness
        • Source Citability
        • Difficulty Level
      • Additional Metrics
        • Guardrails
          • Anonymize
          • Deanonymize
          • Ban Competitors
          • Ban Substrings
          • Ban Topics
          • Code
          • Invisible Text
          • Language
          • Secret
          • Sentiment
          • Factual Consistency
          • Language Same
          • No Refusal
          • Reading Time
          • Sensitive
          • URL Reachability
          • JSON Verify
        • Vulnerability Scanner
          • Bullying
          • Deadnaming
          • SexualContent
          • Sexualisation
          • SlurUsage
          • Profanity
          • QuackMedicine
          • DAN 11
          • DAN 10
          • DAN 9
          • DAN 8
          • DAN 7
          • DAN 6_2
          • DAN 6_0
          • DUDE
          • STAN
          • DAN_JailBreak
          • AntiDAN
          • ChatGPT_Developer_Mode_v2
          • ChatGPT_Developer_Mode_RANTI
          • ChatGPT_Image_Markdown
          • Ablation_Dan_11_0
          • Anthropomorphisation
      • Guardrails
        • Competitor Check
        • Gibberish Check
        • PII
        • Regex Check
        • Response Evaluator
        • Toxicity
        • Unusual Prompt
        • Ban List
        • Detect Drug
        • Detect Redundancy
        • Detect Secrets
        • Financial Tone Check
        • Has Url
        • HTML Sanitisation
        • Live URL
        • Logic Check
        • Politeness Check
        • Profanity Check
        • Quote Price
        • Restrict Topics
        • SQL Predicates Guard
        • Valid CSV
        • Valid JSON
        • Valid Python
        • Valid Range
        • Valid SQL
        • Valid URL
        • Cosine Similarity
        • Honesty Detection
        • Toxicity Hate Speech
    • Prompt Playground
      • Concepts
      • Single-Prompt Playground
      • Multiple Prompt Playground
      • Run Evaluations
      • Using Prompt Slugs with Python SDK
      • Create with AI using Prompt Wizard
      • Prompt Diff View
    • Synthetic Data Generation
    • Gateway
      • Quickstart
    • Guardrails
      • Quickstart
      • Python SDK
    • RagaAI Whitepapers
      • RagaAI RLEF (RAG LLM Evaluation Framework)
    • Agentic Testing
      • Quickstart
      • Concepts
        • Tracing
          • Langgraph (Agentic Tracing)
          • RagaAI Catalyst Tracing Guide for Azure OpenAI Users
        • Dynamic Tracing
        • Application Workflow
      • Create New Dataset
      • Metrics
        • Hallucination
        • Toxicity
        • Honesty
        • Cosine Similarity
      • Compare Traces
      • Compare Experiments
      • Add metrics locally
    • Custom Metric
    • Auto Prompt Optimization
    • Human Feedback & Annotations
      • Thumbs Up/Down
      • Add Metric Corrections
      • Corrections as Few-Shot Examples
      • Tagging
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
      • Evaluation Deployment Guide
        • Evaluation Maintenance Guide
    • Fine Tuning (OpenAI)
    • Integration
    • SDK Release Notes
      • ragaai-catalyst 2.1.7
  • RagaAI Prism
    • Quickstart
    • Sandbox Guide
      • Object Detection
      • LLM Summarization
      • Semantic Segmentation
      • Tabular Data
      • Super Resolution
      • OCR
      • Image Classification
      • Event Detection
    • Test Inventory
      • Object Detection
        • Failure Mode Analysis
        • Model Comparison Test
        • Drift Detection
        • Outlier Detection
        • Data Leakage Test
        • Labelling Quality Test
        • Scenario Imbalance
        • Class Imbalance
        • Active Learning
        • Image Property Drift Detection
      • Large Language Model (LLM)
        • Failure Mode Analysis
      • Semantic Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Active Learning
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Data Leakage Test
        • Outlier Detection
        • Label Drift
        • Semantic Similarity
        • Near Duplicates Detection
        • Cluster Imbalance Test
        • Image Property Drift Detection
        • Spatio-Temporal Drift Detection
        • Spatio-Temporal Failure Mode Analysis
      • Tabular Data
        • Failure Mode Analysis
      • Instance Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Label Drift
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Near Duplicates Detection
      • Super Resolution
        • Semantic Similarity
        • Active Learning
        • Near Duplicates Detection
        • Outlier Detection
      • OCR
        • Missing Value Test
        • Outlier Detection
      • Image Classification
        • Failure Mode Analysis
        • Labelling Quality Test
        • Class Imbalance
        • Drift Detection
        • Near Duplicates Test
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Image Property Drift Detection
      • Event Detection
        • Failure Mode Analysis
        • A/B Test
    • Metric Glossary
    • Upload custom model
    • Event Detection
      • Upload Model
      • Generate Inference
      • Run tests
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
  • Support
Powered by GitBook
On this page

Was this helpful?

  1. RagaAI Catalyst
  2. RagaAI Metric Library
  3. Marketing Content Evaluation

Topic Coverage

Last updated 7 months ago

Was this helpful?

Exclusive to enterprise customers. to activate this feature.

Objective: The Topic Coverage metric measures the proportion of topics available in the context that are covered by the response, particularly focusing on those specifically mentioned in the prompt. If no specific topic is mentioned in the prompt, the metric evaluates coverage against all topics present in the context. The process involves:

  • If the task mentions a topic explicitly, it is prioritized.

  • Topics from the context are then filtered based on the task's requirements.

  • If no topic is specified in the task, all context topics are considered.

  • Each topic's coverage and prominence are then checked against the topics identified earlier.

Required Columns in Dataset:

  • Prompt: The original request or topic that led to the creation of the response.

  • Context: The background information or source material that contains the topics to be covered.

  • Response: The content generated by the model that is being evaluated for topic coverage.

Score Range: 0 (poor topic coverage) to 1 (high topic coverage)

Additional Information: Reasons for the score are provided along with the metric value to help understand how well the response covers the relevant topics.

Code Implementation

experiment_manager = Experiment(project_name="project_name",
metrics = [
    {"name": "Topic Coverage", "config": {"model": "gpt-4o-mini", "provider":"azure"}, "column_name":"Response_Correctness_v2"},
    {"name": "Topic Coverage", "config": {"model": "gpt-4o-mini", "provider":"openai"}, "column_name":"Response_Correctness_v2"}
]

Example:

Prompt: Discuss the key benefits of cloud computing in business, focusing on cost savings, scalability, and security.

Context: The context provided covers various aspects of cloud computing, including cost savings, scalability, security, disaster recovery, collaboration, and access to new technologies.

Response: Cloud computing offers significant benefits for businesses, particularly in terms of cost savings and scalability. By moving to the cloud, businesses can reduce their IT expenses and scale their operations as needed.

Metric Score: Score: 0.4/1.0

Reasoning:

  • Incomplete Coverage: The response covers only two of the three topics explicitly mentioned in the prompt (cost savings and scalability) and fails to address security, which was also specified. Additionally, it does not touch on other relevant topics from the context, such as disaster recovery or collaboration.

  • Prominence: The topics covered (cost savings and scalability) are addressed, but not in-depth enough to reflect their prominence in the context.

Interpretation: The low score indicates that the response does not fully cover the key topics specified in the prompt and misses other important topics from the context. To improve the score, the response should be expanded to address all the relevant topics more comprehensively.

Contact us