RAG Chunk Previewer
Preview how your documents will be chunked for RAG (Retrieval-Augmented Generation). Adjust chunk size and overlap.
Retrieval-Augmented Generation (RAG) is a technique that combines large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems first retrieve relevant documents from a knowledge base, then use those documents as context for generating answers. The chunking strategy you choose directly affects retrieval quality. Chunks that are too large reduce precision — the retrieved chunk contains the answer but also a lot of irrelevant content.
s the answer but also a lot of irrelevant content. Chunks that are too small may lack sufficient context for the model to generate a useful answer. Common chunk sizes range from 256 to 1024 characters, with overlap of 10–20% to ensure context continuity across chunk boundaries.
RAG Document Chunking: How to Split Text for Retrieval-Augmented Generation
Why chunk size and overlap affect RAG retrieval quality, optimal chunk sizes for different content types, and how to preview chunking before building your pipeline.