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