What this tool does
RAG Chunking Tester helps experiment with chunk sizes, overlap, and splitting rules for retrieval workflows.
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🧱 RAG Chunking Strategy Tester
Test chunk size, overlap, and separators before embedding documents for RAG. Preview chunks, token estimates, and query matches.
How to choose chunk settings
Smaller chunks improve precision but can lose context. Larger chunks preserve context but cost more and may dilute retrieval. Use overlap for definitions, tables, and multi-step procedures that span chunk boundaries.
Why it is useful
It helps AI builders understand how documents break into chunks before embedding or search indexing.
How it works
The browser splits pasted text using selected chunk size, overlap, or separator rules and displays the resulting chunks.
Best input
Use representative source documents, not only short clean examples.
RAG tip
Evaluate retrieval quality with real questions; neat-looking chunks are not enough.
Privacy note
Chunk testing happens locally in your browser.
Important limitation
Good chunking depends on document structure, embedding model, retrieval method, query style, and answer requirements.
Quick answers
Is bigger chunk size better?
Not always. Bigger chunks preserve context but can reduce precision.
Why use overlap?
Overlap can preserve context across boundaries, but too much overlap wastes tokens and storage.
RAG context preparation workflow
Move from raw Markdown and logs to tested retrieval chunks, token counts, and a final context-window budget for agent debugging.
Common Use Cases
Test different chunking strategies for RAG
Experiment with paragraph, sentence, or fixed-size chunking to see how it affects retrieval quality.
Evaluate overlap settings for better context
Test how different overlap percentages between chunks impact answer relevance in retrieval-augmented generation.
Compare chunking methods before production
Quickly validate which chunking approach works best for your specific documents before deploying a RAG system.
RAG context preparation workflow
Move from raw Markdown and logs to tested retrieval chunks, token counts, and a final context-window budget for agent debugging.