AI Tools tool

RAG chunk size planner

Pick chunk size and overlap based on answer quality, citation usefulness, and context cost.

Fast answerA retrieval starting point with chunk size, overlap, metadata, and evaluation checks.

Inputs

What to collect

Source type	Docs, tickets, meetings, contracts, policies, or code.
Answer style	Precise citation, summary, troubleshooting, comparison, or generation.
Metadata	Owner, product, version, date, customer, repo, or permission boundaries.

Method

How to use it

1	Chunk around semantic boundaries before tuning token counts.
2	Add overlap only where answers regularly span sections.
3	Evaluate retrieval hits before judging model quality.

Output

How to read the result

Support docs	Medium chunks	Keep steps and warnings together so answers cite complete procedures.
Meetings	Speaker-aware chunks	Preserve decisions, owners, timestamps, and follow-ups.
Code	Symbol and file-aware chunks	Keep function context and repo metadata close to the answer.

Thresholds

Useful vs risky

Healthy	Top results include enough context to answer without flooding the prompt.
Risky	Chunks are tuned by token count only and ignore permissions or source dates.

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