If important qualifications are separated from the retrieved claim, start by preserving one representative example. It gives the investigation a concrete reference and makes the eventual correction easier to judge.
Find the likely cause
Source splitting removes conditions and exceptions.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Keep related limitations accessible with the main passage.
Retrieval helps drafting only when the selected material is relevant, current enough for the task, and identifiable to a reviewer.
Check that the repair worked
Ensure the final answer preserves the relevant conditions.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: the AI retrieves an archived offer instead of the current one. Label active and archived records and prefer the appropriate current source.
Monitor the useful outcome
Track source relevance, missing context, and answer corrections caused by retrieval failures.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.