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Chunking Strategy

The choice of how to split source documents into smaller pieces before embedding them for RAG retrieval. Affects retrieval accuracy more than any other RAG decision.

What Is Chunking Strategy?

Common strategies: fixed-size chunks (e.g. 512 tokens with 50-token overlap), semantic chunks (split at section or paragraph boundaries), agentic chunks (LLM decides splits), and hierarchical chunks (small + parent-doc lookup). Wrong chunking is the top cause of RAG returns garbage problems: too small loses context, too large dilutes the embedding.

How Groovy Web Uses This

We tune chunking strategy per client corpus. Legal docs need section-boundary chunks, code repos need function-level, and product docs work with semantic + parent-doc.

Related Terms

Embeddings (AI) Retrieval-Augmented Generation (RAG) Vector Database

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