Streamline RAG and model evaluation with Amazon Bedrock (AIM359)

Summary of Key Takeaways

Model Evaluation with Amazon Bedrock

  1. Types of Evaluation:

    • Human Evaluation: Leveraging your own team or AWS managed service
    • Programmatic Evaluation: Using metrics like BERT Score, F1 Score, etc.
    • LLM as a Judge: Leveraging language models to provide natural language explanations and scores
  2. Key Features:

    • Use curated or your own data sets for evaluation
    • Automatic and human-in-the-loop evaluation options
    • Predefined and custom metrics for quality and responsible AI
    • Easy to set up and get results in just a few clicks
    • Compare evaluation results across jobs

Retrieval Augmented Generation (RAG) Evaluation with Amazon Bedrock Knowledge Bases

  1. Challenges with RAG Evaluation:

    • Ensuring relevance of knowledge base data
    • Optimizing retrieval process to fetch the right information
    • Evaluating the quality and coherence of the generated response
  2. Key Features:

    • Evaluate retrieval alone or end-to-end retrieval and generation
    • Use LLM as a Judge technology for quality and responsible AI metrics
    • Integrate with Bedrock Guard Rails for safety and trust
    • Easy setup and comparison of evaluation results across jobs
  3. Evaluation Metrics:

    • Retrieval: Context Coverage, Context Relevance
    • Generation: Correctness, Completeness, Helpfulness, Logical Coherence, Faithfulness
    • Responsible AI: Answer Refusal, Harmfulness, Stereotyping
  4. Transparency and Explainability:

    • Judge prompt templates available in documentation
    • Natural language explanations for scores
    • Detailed distribution and prompt-level views

Getting Started

  • Try out the new evaluation features in Amazon Bedrock today
  • Reach out to your account managers or solution architects for support
  • Leverage the public documentation to get started

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