RAG Chunking & Retrieval Budget Planner
Estimate RAG chunk counts, embedding-token budgets, retrieved context and approximate per-query context cost.
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The form starts with hypothetical examples. Replace them with your own measurements or assumptions. Inputs and exports stay in this browser.
What decision does this help answer?
Will our document chunking strategy fit the retrieval budget and model context window?
Calculation and methodology
Estimate chunks per document from document tokens, chunk size and overlap with stride = chunk size − overlap. Multiply by document count for an approximate index size and embedding token budget. Estimate retrieved context as top-k multiplied by chunk size, then compare with context and retrieval-token price inputs.
Limitations and responsible interpretation
Actual document length distributions, metadata, embedding provider behavior and deduplication can materially change totals. More chunks are not automatically higher-quality retrieval, and overlap can inflate repeated context. Measure retrieval accuracy and latency on a labeled query set.
Research and supporting guidance
This planning tool complements Ninth Post's related AI product analysis. Consult the underlying technical guidance or official framework where applicable, and see our editorial methods. The tool does not independently validate submitted assumptions, current vendor prices or legal obligations.