Retrieval-Augmented Generation and Knowledge Integration
Core concepts
Understand the RAG pipeline end to end: ingestion, chunking, embedding, vector storage, retrieval, reranking, and prompt assembly. Know Amazon Bedrock Knowledge Bases: data source connectors (Amazon S3, Confluence, SharePoint, web crawler), the supported vector stores (Amazon OpenSearch Serverless, Aurora PostgreSQL pgvector, Neptune Analytics, Pinecone, Redis Enterprise Cloud), sync jobs, and how chunking strategy (fixed-size, hierarchical, semantic, none) affects recall. Understand hybrid search combining keyword and vector similarity, metadata filtering, and reranking to improve precision. Know how citations and source attribution are surfaced, and how to evaluate retrieval quality separately from generation quality.
Traps
- Assuming larger chunks always improve answers; oversized chunks dilute embeddings and waste context tokens.
- Forgetting that a knowledge base must be re-synced after source documents change.
- Confusing retrieval failure with model hallucination; questions often describe correct-sounding but wrong answers caused by poor chunking.
- Ignoring metadata filters when the corpus contains tenant-specific data.
- Believing RAG eliminates the need for guardrails.
Worked example
A support bot answers policy questions incorrectly for one product line. Rule of thumb: first measure retrieval recall with a labeled question set; if relevant chunks are missing, fix chunking or add hybrid search and reranking before touching the prompt or model.
Rule of thumb
Debug RAG in order: retrieval, then context assembly, then generation. Most wrong answers are retrieval problems.