To keep AI-generated code aligned with your standards, give the coding tool concise, discoverable project guidance; enforce critical requirements with tests and other automated checks; and review its changes through your normal process. Then repeat a representative task to see whether the guidance actually improves the result.
Start with a recurring problem, not a rulebook
Choose a concrete failure that has happened more than once: code placed in the wrong directory, an incorrect test command, an unapproved dependency, or an error-handling pattern that conflicts with the project. Record what the agent changed, what checks it ran or skipped, and what a developer had to correct.
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Use that failure to define a representative task and a success criterion. For example, success might mean that a change lands in the expected directory, uses the project’s established error-handling approach, and passes the correct test command. A specific target makes it easier to tell whether a new instruction helped.
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Keep project instructions concise and grounded in how the repository actually works. Include the information an agent cannot reliably infer from the code alone:
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- Architecture and the purpose of important directories.
- Preferred frameworks, libraries, and dependencies to avoid.
- Naming, error-handling, testing, security, and documentation conventions.
- Accurate build, test, lint, and formatting commands.
- What must be validated before a change is considered complete.
Do not copy rules that are already maintained elsewhere, leave outdated commands in place, or allow instructions to contradict each other. Put a requirement that applies only to one task in that task’s prompt, rather than making it permanent repository policy.
Choose scope based on who and what a rule covers
For GitHub Copilot, GitHub documents three relevant locations: .github/copilot-instructions.md for repository-wide review guidance, a root-level AGENTS.md for project context, and .github/instructions/**/*.instructions.md for path-specific review instructions. Copilot code review reads these instructions from the pull request’s head branch. See GitHub’s code review documentation for the current behavior.
These filenames and discovery rules are not universal. Check the documentation for the coding tool and workflow your team actually uses before choosing a location or assuming an instruction applies. GitHub says organization-level instructions can set a broad baseline, while repository instructions provide more specific requirements and apply in more places; organization instructions apply only on the GitHub website. Details are in GitHub’s guidance on maintaining codebase standards.
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Enforce important requirements with automated checks
Instructions tell an agent what the project expects; automated checks make important requirements repeatable. Run the appropriate tests, formatters, linters, and type checks in CI, and require the relevant workflows to pass before merging. Where suitable, add code scanning, secret scanning, and secret push protection, and require code-scanning results.
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Keep branch protections, pull request approvals, and code-owner review for sensitive areas. These controls catch different classes of problems: a style instruction may steer generated code, while a formatter can enforce formatting consistently and a test can check behavior. No single layer substitutes for the others.
Review every change and test the instructions themselves
Use your normal pull request review even if an AI tool has also reviewed the change. GitHub describes its CLI security review as a lightweight check and recommends continuing standard pull request review. If an AI review is configured to run automatically, check whether new pushes trigger another review rather than assuming they do. GitHub’s code review guidance covers its review workflow.
- Confirm the intended instruction file is in a location the tool reads.
- Repeat the same representative task using the same harness, model, tools, task, and relevant context where practical.
- Compare the changes and validation against the success criterion you set before editing the instructions.
- Revise instructions when the result exposes a gap, contradiction, or inaccurate command, then check again.
Finding that a tool loaded an instruction proves only that it was discovered; it does not prove the agent will follow it consistently. Visual Studio Code’s guide to configuring AI for a codebase recommends grounding customization in observed needs and evaluating it against a task.
Set boundaries for actions, not just output
If an agent can edit files, run commands, or reach services, configure technical limits that match the task’s risk. Depending on the tool and setup, these can include execution sandboxing, network policies, approval requirements for higher-risk actions, and audit telemetry. OpenAI describes these controls for its own deployment in Running Codex safely at OpenAI; they are an example, not a universal specification for every coding agent.
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Keep human review and established recovery practices in place. GitHub cautions that even strict guardrails cannot guarantee vulnerable or error-prone code will not be merged. Instructions and controls reduce avoidable mistakes, but they do not make review or testing unnecessary.
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