Stop Guessing Your RAG Chunk Size: A Visual Guide

Retrieval-Augmented Generation (RAG) is entirely dependent on the quality of your retrieved context. Yet, 90% of developers use the default 1000-token chunk size with a 200-token overlap in LangChain or LlamaIndex and hope for the best.

When you split documents blindly, you risk cutting a sentence in half, destroying the semantic meaning of the vector embedding.

The Semantic Chunking Approach

Instead of a fixed size, modern pipelines use semantic chunking. This means splitting by paragraphs, sentences, or even using an LLM to determine topical boundaries. But how do you know if your overlap is sufficient?

Visualizing the Overlap

I built the RAG Chunking Visualizer inside OmniTool Hub to solve exactly this.
You can paste your raw text, adjust the slider for chunk size and overlap, and instantly see the boundaries highlighted in different colors.

Don't let bad chunking ruin your vector database. Try visualizing it first!