Text Chunk Visualizer
Visualize how your text will be chunked with different settings for RAG and semantic search.
Understanding Text Chunking
Text chunking is crucial for RAG systems. Smaller chunks provide more precise retrieval but may lose context, while larger chunks maintain context but may be less precise. Overlap helps maintain continuity between chunks.
Chunking Settings
Simulates how many chunks would be retrieved
Statistics
Generated Chunks (2)
Large Language Models (LLMs) are advanced AI systems trained on massive amounts of text data. They can understand and generate human-like text, making them useful for a wide range of applications. When working with LLMs, it's important to understand how text is processed and chunked. Chunking strategies can significantly impact
text is processed and chunked. Chunking strategies can significantly impact the quality of your results, especially in retrieval-augmented generation (RAG) systems.
Chunking text for RAG
Retrieval-augmented generation starts by splitting your documents into smaller pieces, or chunks, that get embedded and stored for search. Chunking is deceptively important: chunks that are too large dilute relevance and waste context, while chunks that are too small lose the surrounding meaning a passage needs to be useful. The right size and overlap depend on your content and the questions people ask.
This visualizer shows how a document breaks apart under different chunking settings, so you can see the trade-offs before you index anything. Seeing the actual boundaries — and how overlap carries context across them — makes it much easier to tune a retrieval pipeline for accuracy without exploding your vector count and storage cost.
How to use this tool
- Paste a sample document representative of your real content.
- Adjust chunk size and overlap and watch how the boundaries change.
- Aim for chunks that each contain one coherent idea, with enough overlap to preserve context.
Frequently asked questions
What chunk size should I use?+
There's no universal answer, but a few hundred tokens per chunk with modest overlap is a common starting point. Test retrieval quality on your own questions and adjust — denser, technical content often benefits from smaller chunks.
Why add overlap between chunks?+
Overlap repeats a little text at chunk boundaries so an idea split across two chunks isn't lost. It improves retrieval recall at the cost of a slightly higher vector count.