RAG Chunk Splitter

Retrieval-augmented generation connects a language model to trusted source material. Instead of asking the model to rely only on patterns learned during training, a RAG system searches a document collection and adds the most relevant passages to each prompt. The quality of retrieval depends heavily on how documents are divided. Chunks that are too small can lose context and separate a claim from its explanation. Chunks that are too large can contain several unrelated ideas, making similarity search less precise. A practical starting point for general business documents is 512 to 1,024 tokens. Overlap helps preserve meaning across boundaries. When one chunk ends halfway through an argument, the next chunk repeats a small amount of preceding text. This improves continuity, although excessive overlap creates duplicate results and increases embedding storage. Good chunking also respects document structure. Headings, paragraphs, tables, and code blocks provide meaningful boundaries. A policy manual may work best when split by section, while support articles may be divided by question and answer. Source metadata should travel with every chunk so retrieved passages can be cited and filtered. Evaluation is essential. Teams should test real questions, inspect which chunks were retrieved, and measure whether the final answer is supported by those passages. The best settings depend on writing style, document length, embedding model, and the complexity of user questions.