Amazon Bedrock now offers a systematic method for evaluating Retrieval Augmented Generation systems. The process centers on automatic assessment of your knowledge base's performance, moving beyond basic construction to focus on continuous refinement. This is critical because RAG systems need consistent evaluation to maintain accuracy and relevance, directly addressing LLM hallucination and outdated information issues.

The guide outlines essential prerequisites, including an AWS account and familiarity with S3 and IAM. It recommends using US East (N. Virginia) or US West (Oregon) regions and the Amazon Nova Micro v1.0 model for initial evaluations due to its cost-effectiveness. The workflow involves creating a knowledge base with Titan Text Embeddings v2, syncing data from S3, and running test queries before formal assessment.

Developers create evaluation jobs by preparing a `batchinput.jsonl` file with prompts and reference responses. Amazon Bedrock then uses an evaluator model like Nova Micro to score retrieval quality against metrics like context relevance and coverage. Results are analyzed via CloudWatch, tracking key performance indicators such as InvocationLatency and token counts, which directly impact cost and user experience.