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While an AI coding assistant works, use a separate critique pass to challenge its proposed change. Ask for specific possible bugs, edge cases, risky assumptions, and code locations, then verify those claims with tests, tools, and your own review. Treat the exchange as a way to find questions—not as a vote that proves the code is correct.
What an AI code debate can—and cannot—tell you
“Make the AI argue with itself” is a useful shorthand for asking one model or agent to critique another’s code, or to have the original assistant examine its own work from a skeptical perspective. The point is to expose plausible failure paths while the change is still fresh and bounded.
OpenAI has discussed debate as a proposed safety technique: agents present competing arguments, and a human judges which is stronger. That makes competing reasoning more inspectable; it does not establish that the winner is true or that debate reliably catches software defects. OpenAI’s separate discussion of AI-written critiques also cautions that people can struggle to assess difficult outputs, so human judgment is important but not infallible. OpenAI on AI-written critiques and OpenAI on debate.
Use the result as a list of claims to check. A critique that points to a specific branch, input, or state transition is more useful than a general warning that the code “may have bugs.” A rebuttal from the coding assistant is another generated argument—not independent evidence that the concern is resolved.
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A practical workflow for AI code review
- Bound the change. Ask the coding assistant to make one focused change, and give it the relevant files, behavior requirements, constraints, and tests. Smaller changes are easier to inspect than broad, intertwined edits.
- Request a skeptical critique. Ask a separate reviewer—or the same assistant in a distinct review pass—to look for likely bugs, unhandled edge cases, incorrect assumptions, and security or data-integrity risks where relevant. Provide the changed code and enough surrounding context to understand its role.
- Require actionable findings. For each concern, ask for the file and code location, the condition that triggers it, the plausible failure path, and why it matters. Ask the reviewer to distinguish blockers from lower-priority suggestions and to say when it has no concrete finding.
- Ask the author to respond. Have the coding assistant address each finding with evidence from the implementation or tests. Keep unresolved concerns visible; a confident-sounding rebuttal does not settle them.
- Check the claims outside the conversation. Run relevant tests and applicable static analysis or other checks. Inspect high-impact paths yourself, and involve a human reviewer who understands the system when the change could affect security, data integrity, or product behavior.
- Decide whether the change is ready. Review whether the implementation meets the actual requirements and fits the surrounding system. A clean-looking debate or passing test suite does not replace that decision.
This workflow adapts the idea of critique followed by feedback explored in Microsoft Research’s CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing and Martin Fowler’s guidance on focused review prompts. It is a practical synthesis, not a tested protocol guaranteed to improve code quality.
Choose the review method that fits the change
| Approach | What it can contribute | What to watch for |
|---|---|---|
| Same-model self-critique | A quick second pass over the code, especially when asked to inspect specific risks. | The reviewer may share the author’s assumptions or miss the same issue. Findings still need verification. |
| Separate model or agent | A reviewer role distinct from the coding pass, which can make it easier to ask adversarial questions. | It may lack repository or architectural context. Independence of role does not establish correctness. |
| Tests and other tools | Executable feedback about behavior covered by tests, or issues detected by the tools used. | Checks only cover their inputs, rules, and scope. Passing them is not proof of overall correctness. |
| Pull-request review | A place to inspect a proposed change, discuss findings, and record a team decision. | A pull request is a review mechanism, not a substitute for focused changes, testing, or ongoing refinement. |
| Ongoing team refinement | Feedback as code evolves, without making a pull request the only moment when review happens. | It still needs clear ownership and a decision about whether the change fits the system. |
These approaches differ in reviewer independence, context, use of executable checks, timing, and who makes the final decision. The cited sources discuss their methods and trade-offs; they do not provide a head-to-head trial showing which approach produces the best AI-assisted code review. For prompt design, see Fowler’s Sensible Defaults. For review practices and smaller changes, see Goto Fail, Heartbleed, and Unit Testing Culture and Pull Request.
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How to ask for a useful critique
Give the reviewer a focused scope and request findings that can be checked. For example:
Review this change for likely bugs, unhandled edge cases, incorrect assumptions, and security or data-integrity risks relevant to the code. Use the requirements and surrounding code below. For every concrete finding, include the file and location, the triggering condition, a plausible failure path, and why it matters. Separate blockers from suggestions. Do not report generic possibilities; if you find no concrete issue, say so. Do not assume that tests passing proves correctness.
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Include the expected behavior, relevant constraints, and surrounding code—not just an isolated snippet if the change depends on callers, state, or data flow. This follows Fowler’s advice to supply explicit context, focus review commands, and request structured findings. The prompt helps make a critique inspectable; it cannot ensure that the reviewer notices every defect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where AI debate fits in a team review
Use AI review to generate questions and possible counterarguments, especially during implementation or before a pull request. Keep the change small enough for a person to understand, and use the team’s normal process to decide whether it belongs. Pull requests are one way to review work, not the only one; testing and ongoing refinement also matter. A GitHub project called adversarial-review illustrates one multi-agent approach, but the project description is an implementation example, not independent evidence that debate improves code quality.
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