Choosing a chunking strategy based on model and retrieval evaluation results

Chunking strategy directly shapes retrieval quality and generation accuracy in RAG applications, so it must be chosen based on document structure, embedding model context limits, and retrieval evaluation results rather than a fixed default. Engineers should test multiple chunk sizes/overlaps and measure their effect on retrieval relevance and downstream LLM answer quality before finalizing a strategy.

1 · Learn the must-know

• 1Chunk size must respect the embedding model's maximum input token/context length, since text exceeding it gets truncated and loses information.
• 2Smaller chunks improve retrieval precision (less irrelevant content per chunk) but can lose surrounding context needed for the LLM to generate a complete answer.
• 3Larger chunks preserve more context but risk diluting the embedding's relevance to a specific query and consuming more of the LLM's context window during generation.
• 4Overlapping chunks (a sliding window with some shared text between consecutive chunks) help preserve continuity of ideas that span chunk boundaries.
• 5Document structure should inform chunking: splitting by semantic units like paragraphs, sections, or headers often outperforms naive fixed-length character/token splitting.
• 6The best chunking strategy should be selected empirically by evaluating retrieval metrics (e.g., relevance/recall of retrieved chunks) and end-to-end answer quality, not chosen a priori.

2 · Check your understanding