Stop Guessing Your RAG Chunking Strategy. Inspect It

If you've built a RAG pipeline, you know chunking isn't just splitting text into smaller pieces.

Chunk size affects context retention. Overlap introduces duplication. Splitting on arbitrary boundaries can separate code, paragraphs, or related ideas. And a configuration that looks reasonable on paper might perform poorly during retrieval.

Usually, figuring this out means tweaking parameters, rerunning scripts, dumping chunks to the console, and comparing outputs manually.

I built a tool to make that workflow easier.

Meet the "AabiTech RAG Chunking Simulator" (https://aabitech.com/tools/rag-chunking-simulator) — an interactive workspace for experimenting with chunking strategies before integrating them into your RAG pipeline.

What you can do

• Compare strategies: Experiment with fixed-size, recursive, sentence-aware, paragraph-aware, Markdown-aware, and other splitting approaches.
• Inspect boundaries: Visualize how source text is divided and examine individual chunks and overlapping regions.
• Measure trade-offs: Review chunk counts, size distributions, overlap, and estimated token and embedding costs.
• Explore retrieval: Test lightweight keyword retrieval, parent-child chunking, and context-window budgets.
• Export your results: Download chunks in JSON, JSONL, CSV, or text formats, and generate Python splitter examples for supported frameworks.
• Save experiments: Keep configuration snapshots in your browser or share settings through URL hashes.

Built with privacy in mind

The chunking engine processes your source text locally in the browser instead of sending it to a dedicated chunking API.

That makes it useful for experimenting with documentation, code snippets, and sample datasets without unnecessarily uploading them elsewhere.

What it's useful for

This isn't a replacement for evaluating retrieval quality against your real dataset. It's a way to inspect chunking behavior, understand configuration trade-offs, and narrow down the settings worth testing in your actual pipeline.

If you're building RAG systems, working with vector databases, or experimenting with document retrieval, this workspace may save you some debugging time.

🔗 Try it and bookmark it: https://aabitech.com/tools/rag-chunking-simulator

One question for fellow RAG developers: How do you choose chunk size and overlap — fixed defaults, document-specific rules, or evaluation-driven tuning?

I'd love to hear what works in your pipelines and what you'd like to see added to the tool.