Claupt guide · 8 minute read
How to Chunk Text for RAG and Long AI Prompts
Choose chunk sizes and boundaries that keep related ideas together and improve retrieval quality.
The purpose of chunking
Chunking divides a long source into units that can be indexed, retrieved, or processed separately. Good chunks are small enough to search precisely and large enough to preserve the context needed to understand a passage.
Start with natural boundaries
Headings, paragraphs, and sentences usually make better boundaries than fixed character positions. A paragraph often contains one argument or event. Hard splitting is useful only when a strict size limit must be enforced, because it can divide a sentence or identifier.
Choose a target size
There is no universal best size. Short factual entries may work as small chunks, while contracts, research explanations, and code documentation often need larger sections. Start with a moderate target, evaluate retrieved results, then adjust. The embedding model, query style, and answer task all matter.
Use overlap with a reason
Overlap repeats a small part of one chunk in the next and can preserve context at a boundary. Excessive overlap increases storage and can return duplicate evidence. Use it when ideas regularly span paragraphs, then measure whether retrieval improves.
Attach metadata
Store the document title, section heading, source URL or file, date, and a stable chunk identifier. Metadata supports filtering and citations. Do not rely on the chunk text alone to explain where a statement came from.
Evaluate with real questions
Create questions that your users will actually ask. Check whether the correct chunk appears near the top, whether it contains enough evidence, and whether unrelated chunks outrank it. Review answers for unsupported claims. Chunking is part of a retrieval system, so evaluate retrieval and generation together.
How to validate your result
Keep the original and transformed versions while you test. Select several representative examples, including a typical case, a long or complicated case, and an input with unusual formatting. Run the same task with each version and compare factual accuracy, completeness, formatting, latency, and reported token usage. A transformation is useful when it reduces avoidable material without changing the evidence or instructions needed for a correct answer.
Do not judge the workflow from one successful response. AI output varies, so repeat important tests and review failure cases. For automated work, add checks for required fields and expected source references. Record the tool settings used so the result can be reproduced. When exact limits, billing, privacy, or compliance matters, confirm the result against the current documentation of the AI provider and your organization’s policy.
Apply this workflow locally in your browser and inspect the result before use.