RAG Chunk Size Calculator – Optimal Chunking for LLMs

How Chunking Works in RAG

In Retrieval-Augmented Generation, your documents are split into chunks, embedded into vectors, and stored in a vector database. At query time, the most relevant chunks are retrieved and inserted into the LLM's context window alongside your question. The chunk size must balance:

• Too small: Chunks lose context and semantic meaning
• Too large: Fewer chunks fit in context, reducing coverage

Important Notes

• Client-side processing only — no data sent to server.
• Recommendations are guidelines. Optimal size varies by content and retrieval strategy.
• For code, prefer function/class-level chunking over fixed token counts.
• Free to use with no login/signup required.
• Report bugs in comments with sample input and expected output.

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