Question

You are building a RAG application over a large corpus of software engineering documentation that mixes prose explanations with fenced code blocks and API tables. During testing, users complain that when they ask 'show me the function that validates tokens,' the retrieved chunks return only the surrounding explanation text and cut code samples in half, making the answers useless. You are currently using a fixed-size character chunker with 500-character chunks and no overlap. Which change to your chunking strategy will most directly improve retrieval of complete, coherent code examples?

Accepted Answer

Switch to a structure-aware (recursive/markdown-aware) chunker that respects code-fence and section boundaries, and add chunk overlap to preserve context.

Answer

Reduce the chunk size to 200 characters so more granular chunks are indexed and code is easier to match.

Answer

Increase the number of results (top_k) returned by Vector Search from 3 to 50 so the code block is eventually included.

Answer

Replace the embedding model with a larger general-purpose text embedding model to better understand code semantics.