A practical workflow for revising a manuscript with AI help (short version)

A practical workflow for revising a manuscript with AI help (short version)

1. Start by gathering the full review package

Give the AI the manuscript, the full reviewer comments, and any materials the reviewers are actually referring to, such as supporting information, appendices, code repositories, or public specification files. If the AI does not see the same source material the reviewer saw, it may produce edits or rebuttal text that sound good but miss the real issue.

2. Decide the revision scope before editing

Before changing anything, decide whether the revision should be light, moderate, or substantial. This helps separate essential corrections from attractive but out-of-scope ideas and keeps the manuscript aligned with the paper type.

3. Turn reviewer comments into an edit plan, not just a summary

Ask the AI to convert each reviewer comment into a concrete plan: what the concern is, how to respond, where the issue should be handled, and what still needs an author decision. This is also the right time to flag items the AI should not guess.

4. Ask for sentence-level edits you can actually apply

Once the plan is clear, ask for exact replacement sentences, insertion text, or short paragraph additions tied to specific parts of the manuscript. This is usually more useful than asking for a full rewrite, especially when you need tracked changes.

5. Make the marked-up manuscript yourself, then bring it back for checking

Apply the edits in tracked changes or another marked-up format, then give that revised file back to the AI. At this stage, the AI should compare the marked manuscript against the revision plan and tell you what was clearly changed, what is missing, and what may need review.

6. Write the rebuttal from the full reviewer comments, not from summaries alone

Build the response letter from the reviewers’ complete comments, not just short summaries. The response should say what was changed, what was clarified, what was left for future work, and what was intentionally not changed.

7. Build a response-to-edit map

For each reviewer comment, connect the proposed response to the specific manuscript edits that support it. This makes the rebuttal stronger and also helps confirm that the manuscript really says what the rebuttal claims it says.

8. Keep a separate list of author-decision items

Some issues should remain open until the author makes a deliberate choice, such as a specialized technical correction, a tone decision, a new table, or a repository update. Let the AI flag those items, but do not let it decide them silently.

9. Do a final consistency pass across all artifacts

Before submission, check the marked-up manuscript, rebuttal letter, supporting-information edits, and any external files together. The goal is to confirm that every major reviewer concern is addressed and that the rebuttal does not promise changes that are missing from the manuscript.

10. Keep the author in charge of the science

Use the AI for structure, drafting, comparison, and clarity, but keep scientific judgment, strategic concessions, and final technical wording under author control. The process works best when the AI improves the workflow without replacing domain expertise.