AI-assisted guide · Reviewed before publication · October 22, 2026
A reviewer often needs a quick orientation before they inspect a change: what it is for, what changed, and where a closer look may help. AI can help turn your notes or a carefully selected diff excerpt into a first-draft summary. Treat that draft as an editing aid, not as evidence that the change is correct. You know the intent and repository context; you must check the summary against both before sharing it.
Prepare a small, safe input
Start with information you are permitted to share with the AI tool. That might be a brief description of the task, a list of files or components changed, or a selected diff excerpt. Remove credentials, secrets, private customer information, and confidential code unless your organization has approved that use. When in doubt, use a generalized description instead of pasting the code. Add context that helps explain intent: what problem the change addresses, what is deliberately out of scope, and any relevant behavior a reviewer should know. Keep facts separate from guesses. If you are unsure whether a behavior changed, say so rather than asking the model to fill in the gap.
Request a structured first draft
Give the model a clear task and specify the format you want. For example, ask for three short sections: purpose, main changes, and reviewer checks or open questions. Set boundaries: it should use only the supplied information, distinguish stated facts from uncertainty, and flag missing context instead of inventing details. A structure makes the draft easier to scan and easier for you to audit. Ask for plain language and concise bullets, but avoid asking it to declare the change safe, correct, or ready to merge. Those are judgments for the responsible people and review process. Clear instructions and context are useful when prompting, but the output can vary; regard the result as a draft to inspect, not a dependable description by default.
Check every sentence against the change
Compare each claim with the task description and the actual diff. Confirm that the stated purpose matches the intended work, file or component descriptions are accurate, and no unmentioned behavior or test result has appeared in the draft. Delete claims you cannot verify. If a question for the reviewer is useful, make sure it is a genuine open question—not a suggestion that the AI has already found a defect. You can also ask the model to identify which parts of its draft are directly supported by the supplied material and which need confirmation. That can help focus your own review, but it does not replace checking the code. If the draft misses an important change, add it yourself.
Fit the summary to your team’s handoff
Use the conventions your team already expects, such as a short overview, testing information you have personally confirmed, and questions that need a reviewer’s attention. Do not have AI claim that tests passed, a requirement was met, or a risk was ruled out unless you verified that statement and want it included. If your team has a standard template, provide only the relevant headings and ask the model to draft within them. For repeated use, try the prompt on a few different kinds of changes and adjust it when it produces vague or unsupported language. Prompt testing and evaluation are sensible practices when building repeatable uses of AI; they do not guarantee that every future summary will be accurate. Keep a final human check in the workflow.
Illustrative example
Suppose a developer provides an anonymized note: “The settings page now remembers the selected display density between visits. The change updates a preference-reading helper and adds coverage for restoring the selection. No other settings are intended to change.” A useful draft might say: Purpose: Preserve the user’s selected display density between visits. Main changes: Update the preference-reading helper and add coverage for restoring the selection. Reviewer checks: Confirm the change is limited to display density and that the added coverage exercises restoration. This is illustrative, not a report of a real project or test result. The developer should compare it with the actual changes, verify that the coverage exists and says what the summary implies, and revise or remove anything unsupported.
