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To make a Codex bug fix easier to verify, give it a reproducible bug report, a clear patch boundary, explicit test requests, exact checks to run, and a final evidence summary. These five adaptable prompts provide that structure. They are not magic phrases: review the resulting changes and test evidence before accepting a fix.
1. Ask Codex to investigate before editing
Start with the facts needed to reproduce the problem: software version and environment, steps to reproduce, actual behavior, and expected behavior. Where possible, include a minimal reproducible example. This mirrors the details requested in the OpenAI Developer Community guidance for Codex bug reports.
Investigate this bug before editing.
Environment and version: [details]
Steps to reproduce: [steps]
Actual behavior: [what happens]
Expected behavior: [what should happen]
First tell me the most likely failure path and what evidence in the code supports it. If you cannot reproduce the bug or identify its cause, say what is missing and stop before proposing a patch.
The stop condition helps keep an uncertain diagnosis from turning prematurely into a code change. It is a useful constraint, not a guarantee that Codex will reproduce the issue or identify the cause correctly.
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Describe the behavior to change and where the fix should stay. Naming a component or files makes the eventual diff easier to assess. Allow an exception when another file is genuinely necessary, but ask Codex to explain before crossing the boundary.
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Fix [specific behavior] with the smallest change that addresses the reproduced cause. Keep the change within [named files or component] unless you explain why another file is necessary. Do not change unrelated behavior. Before editing outside that boundary, report the reason.
Explicit constraints make the intended scope easier to compare with the actual diff; they do not ensure the agent will follow every instruction. OpenAI’s Codex Goals guide describes a completion condition as including what should be true, how success should be checked, and which constraints must remain intact.
3. Request a regression test and relevant edge cases
A fix is more convincing when a test captures the reported failure and would fail without the fix. Ask Codex to follow the repository’s existing test pattern and name the edge cases or failure paths that matter for this bug.
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Add a regression test that fails on the reported bug and passes with the fix. Also check the relevant edge cases and failure paths, including [specific cases]. Follow the existing test pattern. Tell me which tests you added and why each case matters.
Choose cases based on the failure rather than adding a generic list. OpenAI’s Codex guide notes that engineers often ask Codex for tests covering edge cases or likely failure paths.
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4. Specify the checks and require exact outcomes
Name the test, lint, or build commands that are relevant to the change. Ask for the exact command and an outcome for each, including a clear explanation if a check could not run.
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Run [specific test command] and [any relevant lint/build command]. Report the exact commands and whether each passed, failed, or could not run. If a check was not run, explain why; do not describe an unrun check as passing.
Codex’s report helps you see what verification was attempted, but it is not proof by itself. Check the repository state and test output rather than treating a claimed pass as independently confirmed. The Goals guide supports defining how success will be checked; the actual commands should fit the project.
5. Ask for a reviewable final report
Request a concise map from diagnosis to evidence: the root cause, changed files, behavior change, tests and checks run, and anything that remains uncertain. Ask Codex to point to the relevant code and test evidence, not just declare the task complete.
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Summarize the root cause, changed files, behavior change, tests and checks run, and any remaining uncertainty. Point me to the code and test evidence for the fix. Flag anything you could not verify. Do not claim the bug is resolved unless the relevant reproduction or regression test now passes.
Use the report as a review route, then inspect the changes and the evidence yourself. OpenAI’s pull-request review guidance advises checking changed files, test results, and findings against the relevant code before relying on them.
How to use the prompts together
Adapt the sequence to the bug and repository rather than pasting every template unchanged. A practical order is:
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- Provide the version, environment, reproduction steps, and expected-versus-actual behavior; ask for investigation before edits.
- Once the cause is supported by code evidence, state the intended behavior and patch boundary.
- Request a regression test and bug-specific edge cases.
- Name the commands that should verify the change and require exact outcomes.
- Use the final report to locate the diff and evidence, then review both yourself.
If the bug cannot be reproduced or the cause is unclear, resolve the missing information before authorizing a patch. If a check cannot run, treat that as an unresolved verification gap—not a pass. Verification matters because code checks and review findings can be wrong or incomplete; OpenAI Alignment’s discussion of code verification addresses the risks and costs of incorrect findings.
Quick Recap
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