RAG Chunk Visualizer
See exactly how your document will be split into chunks for vector databases and RAG pipelines.
What is RAG chunking?
Retrieval-Augmented Generation (RAG) is a technique where AI models retrieve relevant context from a vector database before answering a question. However, language models have limited context windows and vector databases perform best when searching through smaller, focused snippets of text.
To solve this, large documents must be split into smaller "chunks" before being embedded. If chunks are too small, the AI loses the surrounding context (e.g., a pronoun with no noun). If chunks are too large, the search precision drops and costs increase. Adding an "overlap" ensures that concepts split across chunk boundaries aren't completely severed.
The best chunks come from clean, structured text. By converting your PDFs, Word documents, and web pages to Markdown before chunking, you can maintain semantic meaning (like headers and lists) and dramatically improve your RAG pipeline's accuracy.