A reviewable AI coding request makes the intended change clear, gives the agent useful repository context, and sets out how someone will verify the result. Treat it as a small work specification: concrete enough to judge, but focused on the task at hand.
1. Describe the problem and the intended outcome
Start with what is wrong or what needs to change, who or what is affected, and the behavior you want instead. A request such as “fix the settings page” leaves the problem and desired result open to interpretation. A stronger request names the observed issue and describes what a user should see after the change.
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OpenAI recommends structuring a Codex prompt like a GitHub issue, and GitHub similarly advises starting with a clear description of the work. These are practical recommendations, not a guarantee that a particular prompt will produce a correct result. OpenAI’s Codex guidance · GitHub’s task-scoping guidance
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Point to the files, components, examples, or documentation that matter when you know them. Include constraints and established project conventions that could affect the implementation. OpenAI lists paths, component names, diffs, and documentation snippets as useful context; naming a likely file is a guide, not a reason to ignore other code the agent needs to inspect.
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For recurring project knowledge, repository guidance such as AGENTS.md can document conventions, business logic, or known quirks. Keep that guidance relevant to the work: instructions should not make an agent consult unrelated material for every change. OpenAI’s Codex guidance · OpenAI Developers’ guidance on contextual instructions and completion
3. Set acceptance criteria a reviewer can check
State the observable behavior that will count as done, including meaningful edge cases. If testing is relevant, say whether tests should be added or updated and what verification is expected. “Make it robust” is difficult to inspect; a criterion describing the expected result for a specific input or failure case gives a reviewer something concrete to assess.
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GitHub’s task guidance calls out a task description, complete acceptance criteria, and file directions as core parts of a well-scoped Copilot task, and recommends clarifying whether unit tests are needed. GitHub Docs
4. Match the workflow to scope and uncertainty
Not every request needs the same process. Use the task’s size, uncertainty, and verification needs to decide whether to ask for a plan before implementation.
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| Task situation | Useful request shape |
|---|---|
| Small, bounded change with known behavior | Give a concise task, relevant context, checkable criteria, and the expected verification. |
| Large or cross-cutting change, or an uncertain approach | Ask the agent to inspect the repository and propose a plan first; agree on an approach, then implement and iterate. |
OpenAI recommends plan-first use for large changes. GitHub describes researching the repository, planning, and iterating before a pull request. A plan-first workflow is especially useful when the right approach is not yet clear; it is not a required ceremony for every small edit. OpenAI’s Codex guidance · GitHub’s workflow guidance
5. Define completion and review boundaries
Say what the agent should report when it finishes: for example, what behavior changed, which checks it ran, and what remains incomplete or limited. Set a stopping point or request human review where the task calls for it. Be explicit about sensitive or high-impact actions that require authorization rather than assuming a coding agent should make them.
OpenAI describes execution controls such as sandboxing, approvals, network policy, and logs as ways to govern agent actions. These controls concern the environment in which an agent runs; a prompt should not be treated as a substitute for appropriate technical boundaries. OpenAI’s guidance on running Codex safely
Acceptance checklist
- Is the problem or desired change stated in concrete terms?
- Is the expected user-visible or system behavior clear?
- Have you included the relevant paths, components, examples, and constraints you know?
- Can a reviewer inspect each acceptance criterion?
- Have you said whether tests or other verification are part of completion?
- Does the work merit a plan or staged iteration before code changes?
- Have you identified sensitive or high-impact actions that need review or authorization?
- Have you defined what the agent should report, including incomplete work or limits?
This checklist is a practical synthesis of vendor guidance, not an official standard. Adapt it to the repository’s actual test setup, permissions, and risks.
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What these habits can—and cannot—promise
OpenAI’s guidance says, “Codex works best when it’s given structure, context, and room to iterate.” GitHub Docs says, “GitHub Copilot provides better results when assigned clear, well-scoped tasks.” These statements describe the vendors’ guidance; neither is a measured result or a quantified guarantee. The available recommendations do not establish a specific success-rate increase, time saving, or improvement percentage from using this checklist.
For general prompt-writing advice on clarity, context, and refinement, see the OpenAI Help Center’s prompt engineering guidance.
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