Document Chunking
Document chunking splits content into small parts that a search system can find. Clear rules set each part's size, overlap, and stopping point before indexing.
Another example
A search engineer splits policy documents that contain headings, tables, and page details. Each chunk keeps its section meaning and source position. Cutting by character count and breaking a table row is a bad strategy.
Main parts
- 01boundary rule
- 02chunk size and overlap
- 03source metadata
Use it when
Use this term when setting the parts used for search indexing, embeddings, or prompt context.
Do not use it when
Do not choose chunk size by token count alone when headings, lists, or tables carry meaning.
Name used in code
document → structure-aware nodes + metadata Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Document Chunking to make AI behavior measurable and tied to evidence. User context: A search engineer splits policy documents that contain headings, tables, and page details. Each chunk keeps its section meaning and source position. Cutting by character count and breaking a table row is a bad strategy. AI method or concept: Document Chunking. Why it fits: Use this term when setting the parts used for search indexing, embeddings, or prompt context. Do not use it when: Do not choose chunk size by token count alone when headings, lists, or tables carry meaning. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Document Chunking. Definition: Document chunking splits content into small parts that a search system can find. Clear rules set each part's size, overlap, and stopping point before indexing. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.