How Do You Use Contextual Retrieval to Enhance RAG Chunks With Metadata?

What Technique Addresses Claude Lacking Context in RAG Systems?

Discover how contextual retrieval enhances RAG systems by prepending document metadata and surrounding context to isolated chunks, ensuring Claude always understands the data.

Question

Your RAG system retrieves relevant chunks but Claude sometimes lacks context about where information comes from. What technique addresses this?

A. Increase chunk size to include more text
B. Retrieve more chunks per query
C. Use contextual retrieval to enhance chunks with document metadata and surrounding context
D. Switch to BM25 instead of vector search

Answer

C. Use contextual retrieval to enhance chunks with document metadata and surrounding context

Explanation

When an AI model like Claude receives a relevant chunk of text but lacks the broader context (such as the document it came from, the section it belongs to, or what the text is referring to), the best solution is to use Contextual Retrieval. This technique solves the issue by having an LLM read the full document and automatically prepend a brief, orienting summary and relevant metadata to each isolated chunk before it is embedded and indexed, ensuring the chunk retains its original meaning and context when it is later retrieved and sent to Claude.