A system may retrieve the paragraph containing a price, policy or procedure while omitting a qualification in the next section. The model then receives an incomplete basis for an otherwise fluent response.
Test the difficult document structures
Include tables, footnotes, headings and cross-references that matter to the workflow. A collection of simple paragraphs may not represent the documents the customer actually uses.
AWS’s chunking documentation describes approaches for dividing material during ingestion. Configuration choices need evaluation against the intended sources rather than selection solely by convenience.
An illustrative product manual may place a safety condition before a numbered procedure. Retrieving only the procedure could remove information required to interpret it correctly.
Inspect retrieval before rewriting prompts
Check which passages were available to the generator. If the relevant exception never entered the context, a stronger instruction to “be accurate” may not solve the problem.
Compare suitable chunking and retrieval configurations using the same evaluation questions. Record changes so results remain interpretable.
More context is not automatically better. Irrelevant or conflicting material can make the evidence harder to use. The objective is sufficient, appropriate context for the question.
Keep the source relationship visible
Retain document identity, section and relevant version metadata where the system supports it. This helps reviewers determine whether a passage was separated from a meaningful condition.
Keep examples of source passages where boundaries matter, including a table heading and its notes. Use them as regression cases when the ingestion configuration changes. A retrieval adjustment that improves simple questions can still harm documents whose meaning depends on relationships between nearby sections rather than isolated semantic similarity.
GAT’s enterprise AI pilot guide asks for workflow-level testing. Chunk-boundary review adds a specific test: can the assistant preserve the meaning of the source when the retrieval system divides it into smaller pieces?
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