If grounded drafting becomes a stitched-together paraphrase, 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
The model summarizes sources without serving a reader task.
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
Organize the article around a question and add original explanation or demonstration.
Grounded drafting keeps the model's factual claims tied to a defined evidence set while leaving room for original explanation.
Check that the repair worked
Identify the useful reasoning contributed beyond the source summaries.
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 draft adds facts beyond the supplied sources. Require unsupported details to be marked as questions rather than asserted.
Monitor the useful outcome
Track supported claims, unresolved questions, and factual corrections per draft.
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.