RAG Chunking Calculator
Size your chunks against your embedding model, context window and retrieval depth.
Runs entirely in your browser · Nothing is sent or stored · No signup
Chunks in the index
27,505
14.1M stored tokens · 1.18× duplication from overlap
Corpus
Per request
What this is for#
Two practical questions before you build: how many chunks will this corpus produce, and how many of them can I actually fit in the context window at retrieval time?
The context budget#
Retrieval depth × chunk size has to leave room for the system prompt, the question and the answer. It is common to discover that a chosen top-k does not fit, and to discover it in production when a long document arrives.
What to spend effort on instead#
Within roughly 256–768 tokens, chunk size has a small effect on retrieval quality — under 0.03 recall in our measurements. Chunking strategy and reranking are worth far more. Use this to check feasibility and cost, then spend your tuning time elsewhere.
Related reading
RAG Evaluation Lab
Most RAG systems are tuned by vibes. This is a reproducible harness that measures retrieval and generation separately, so you can see which half is failing.
Six RAG Chunking Strategies Compared
Fixed, recursive, semantic, structural, sentence-window and parent-document chunking measured on retrieval recall across three corpora.