📚 RAG Chunk Size Calculator
Calculate RAG chunk count, effective stride, overlap duplication, embedding tokens, retrieval context, and approximate embedding cost.
Calculate RAG Chunk Size
What this RAG chunk size calculator calculates
Plan document chunking for retrieval-augmented generation using document tokens, chunk size, overlap, number of retrieved chunks, and embedding price.
Chunk size is a retrieval-quality choice, not only a storage choice. Test answer recall, boundary loss, metadata filtering, and reranking.
RAG Chunk Size Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. AI models, tokenizers, runtimes, accelerators, cloud services, and provider billing rules differ. Validate important decisions with measured data from the exact model, hardware, framework, and pricing plan you intend to use.
Worked example
A 100,000-token document split into 1,000-token chunks with 200-token overlap uses an 800-token stride and about 125 chunks.
How to use the result
Chunk size is a retrieval-quality choice, not only a storage choice. Test answer recall, boundary loss, metadata filtering, and reranking.
- Start with representative production assumptions rather than best-case demos.
- Run a low, expected, and high scenario to understand the range.
- Record model version, pricing date, hardware, precision, and workload details.
- Replace assumptions with observed p50 and p95 measurements after testing.
Common mistakes to avoid
- Choosing overlap greater than or equal to chunk size.
- Optimizing only for low chunk count while losing semantic completeness.
- Ignoring headings, tables, code blocks, and document structure.
Frequently asked questions
What is a good RAG chunk size?
There is no universal value. Common starting ranges depend on document structure, embedding model, retrieval method, and answer granularity.
Why use overlap?
Overlap reduces the chance that important context is split at a boundary, but it increases embedding and storage cost.
Should chunks be fixed-size?
Not always. Structure-aware or semantic chunking can preserve sections better than fixed token windows.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This relationship connects document segmentation and vector retrieval to index size, context use, retrieval quality or generation cost. Assumption: Embedding model, chunk overlap, top-k, corpus characteristics and evaluation labels strongly influence the practical result.