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Write an AI coding request like a small work specification: explain the problem, point the agent to relevant repository context, define observable acceptance criteria, choose a workflow that fits the task, and say what completion evidence to report. That gives both the agent and reviewer a clearer target without turning every request into a long, generic prompt.
Five habits that make a coding request reviewable
1. Describe the problem and the intended outcome
Start with what is wrong or what needs to change, what is affected, and how the behavior should look afterward. Avoid requests that name only an implementation, such as “refactor this,” without saying what problem the refactor is meant to solve. OpenAI recommends shaping a Codex request like a GitHub issue; GitHub likewise advises starting with a clear description of the work.
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OpenAI’s Codex guidance and GitHub’s Copilot task guidance both emphasize a well-scoped task rather than a vague wish.
2. Give the agent useful repository context
Name relevant files, components, examples, and project constraints when you know them. A path to the affected code, a representative diff, or a short documentation excerpt can be more useful than a broad instruction to “follow the project.” Include only context that applies to this change.
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For conventions and recurring business rules, repository-level guidance such as an AGENTS.md file can preserve context across tasks. OpenAI advises keeping persistent instructions relevant rather than making an agent consult unrelated material for every edit. See OpenAI’s guidance on using Codex and its September 11, 2026 guidance on concise, contextual instructions.
3. State acceptance criteria a reviewer can inspect
Describe observable behavior, not just the desired effort. For example, say which input should produce which result, what should happen at a relevant edge case, and whether tests need to be added or updated. Criteria should let someone inspect the change and decide whether it meets the request.
GitHub’s task guidance specifically calls out a task description, complete acceptance criteria, and file directions as core elements of a well-scoped task. Its documentation also recommends making clear whether unit tests are needed. Read GitHub’s guidance.
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4. Match the workflow to the task’s size and uncertainty
A small, bounded change with known behavior may need only a concise task and clear criteria. For a large or cross-cutting change, or when the right approach is uncertain, ask for a plan first, review the approach, then have the agent implement and iterate. GitHub describes research, planning, and iteration before a pull request; OpenAI similarly recommends plan-first work for larger changes.
Choose the workflow by considering:
- Scope: Is this a bounded edit or a change spanning multiple components?
- Uncertainty: Is the expected behavior known, or does the approach need discussion?
- Context: Can the agent locate the relevant code, or should you identify paths and examples?
- Verification: Can tests check the behavior, or will review and other evidence be needed?
- Risk and authority: Does the work touch security, sensitive data, production, or actions that require approval?
These distinctions are reflected in the workflow advice from OpenAI and GitHub.
5. Define completion and review boundaries
Say what the agent should report when it finishes: for example, the behavior changed, tests or checks run, and any remaining limitations. Also state where it should stop, ask a question, or seek human review—especially before sensitive or high-impact actions.
OpenAI’s current guidance emphasizes defining completion before work begins. Its safety guidance describes sandboxing, approvals, network policy, and logs as controls for governing agent actions; those controls establish execution boundaries but do not replace task-specific acceptance criteria. See OpenAI’s September 2026 guidance and OpenAI’s safety overview.
Acceptance checklist before you send the request
- Is the problem or desired change stated concretely?
- Is the expected user-visible or system behavior clear?
- Have you named the most relevant paths, components, examples, and constraints you know?
- Can a reviewer inspect the acceptance criteria and determine whether they are met?
- Have you said whether tests or other verification are part of completion?
- Does the task warrant a plan or staged iteration before code changes?
- Have you identified sensitive or high-impact actions that need authorization or review?
- Have you defined what the agent should report, including unfinished work or limitations?
A practical request pattern
Use this as a structure, not a rigid form. Fill in only the pieces that matter to the task:
Problem: [What is wrong or needs to change, and who or what is affected?]
Outcome: [What should happen after the change?]
Context: [Relevant files, components, examples, and constraints]
Acceptance criteria:
- [Observable expected behavior]
- [Relevant edge case]
Verification: [Tests or other checks to run]
Workflow: [Plan first, or implement directly]
Boundaries: [Actions requiring approval; where to stop and ask]
Completion report: [Changes made, checks run, and remaining limitations]Adapt the pattern to the repository’s actual test setup, permissions, and risk. It is a practical checklist inferred from vendor guidance, not an official standard or a guarantee of a better result. The published advice is qualitative; it does not establish a measured success rate, time saving, or improvement percentage for these habits. For general clarity and iterative refinement, OpenAI also offers prompt engineering guidance for ChatGPT.
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