Estimate chunk counts for your document corpus.
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The RAG Chunk Size Calculator is a free page for reasoning about chunk size in a retrieval-augmented generation pipeline. It focuses on the trade-off between retrieval quality and context-window usage: chunks that are too small lose meaning, while chunks that are too large dilute search and consume tokens. It is aimed at developers tuning document ingestion and retrieval. No sign-up is required. Validate any suggested size with retrieval tests on your own corpus.
Common starting points are a few hundred tokens with modest overlap, but the right value depends on document structure and query type. Technical manuals, contracts and chat logs each behave differently. Evaluate recall and answer quality across several sizes rather than fixing one by convention.
A small overlap helps when an answer spans a boundary and would otherwise be split. Too much overlap increases index size, embedding cost and duplicate retrieval. Start with a modest overlap, then reduce it if retrieval returns near-identical neighbours.
Yes. Smaller chunks mean more vectors to store and search, while larger chunks send more tokens to the model on every query. Both affect spend. Plugsky's embedding and chat APIs are OpenAI-compatible, so you can measure different chunking strategies on the free plan, which includes 2 free models.
Canonical pricing and plans: plugsky.com/#sec-pricing · Terms · SLA · Docs