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To keep AI-generated code aligned with your standards, give the coding tool concise, repository-specific guidance; enforce critical requirements with automated checks; and review and test every change using your normal process. Then repeat a representative task to verify that the guidance is both discovered and useful.
Start with a recurring failure, not a rulebook
Choose a concrete problem the tool has caused more than once: putting files in the wrong directory, using an unapproved dependency, skipping the right test command, or missing a local error-handling convention. Record what the tool changed, which checks it ran or skipped, and what a developer had to correct. This gives each instruction a clear purpose.
Before changing configuration, define a representative task and a success criterion—for example, the right files are changed, an established project pattern is followed, and the relevant tests pass. Guidance is easier to evaluate against a specific outcome than against a vague goal such as “write better code.”
Write concise guidance the tool can discover
Put durable project context in instruction files that the coding harness actually reads. Include only information that is important and not reliably inferable from the codebase or maintained elsewhere:
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- Architecture and the purpose of important directories.
- Preferred frameworks, libraries, and dependency constraints.
- Naming, error-handling, testing, security, and documentation conventions.
- The correct build, test, lint, and formatting commands.
- Required validation before work is considered complete.
Keep instructions accurate and resolve contradictions. A requirement that applies only to one task belongs in that task’s prompt, not in permanent repository guidance. Avoid repeating a policy maintained in another authoritative place; conflicting copies can make the intended rule less clear.
Choose scope and file location deliberately
For GitHub Copilot, GitHub documents .github/copilot-instructions.md for repository-wide code-review guidance, AGENTS.md at the repository root 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 instructions.
Rank #2
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- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
Use a repository-wide file for conventions that apply across the project, and path-specific guidance when areas genuinely differ—for example, generated code versus hand-maintained code. The file names and discovery rules are tool-specific; check the documentation for the harness and surface your team uses rather than assuming another tool reads the same files.
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GitHub also describes organization-level instructions as a broad baseline and repository instructions as more specific requirements that apply in more places. Its rollout guidance says organization instructions apply only on the GitHub website, so confirm where a rule takes effect before relying on it. See GitHub’s codebase standards guidance.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
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- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Make important requirements enforceable
Instructions guide generation and review, but they are not a dependable substitute for checks. Put requirements that can be tested into repeatable automation: run the relevant tests, formatter, linter, and type checker in CI, and require the important workflows to pass before merge.
Where appropriate to the project, add code scanning, secret scanning, and secret push protection, and require review of code-scanning results. Protect important branches with pull requests and approvals; use code owners for sensitive areas. These controls make standards more consistent, but they do not guarantee every defect will be caught.
Rank #4
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Keep human review in the workflow
Review AI-generated changes through the same pull request process as other code. An AI review can be an additional check, not approval to skip developer review, tests, or security controls. 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 instead of assuming they do.
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GitHub warns: “Even with the strictest guardrails in place, it is always possible that vulnerable or error-prone code will be merged, regardless of whether your developers are using AI tools.” Keep ordinary review, testing, and recovery practices in place accordingly.
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
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Verify that instruction changes help
- Confirm that the intended instruction file is present in the location the tool documents and is included in the relevant branch or task context.
- Repeat the representative task with the same harness, model, tools, task, and relevant context as far as practical.
- Compare the result with the success criterion you set: check the files and patterns changed, required commands run, and corrections still needed.
- Revise instructions when they are missed, unclear, outdated, or contradicted by other guidance; then repeat the check.
Finding an instruction file proves only that it was discovered. It does not prove the agent will follow every rule. Treat configuration as something to validate against actual work, not as a one-time guarantee. Visual Studio Code’s guide to configuring AI for a codebase provides further instruction on customizing Copilot for a project.
Set boundaries for agents that can take action
If an agent can edit files, run commands, or access services, set technical limits appropriate to the task. Consider execution boundaries, network controls, approval requirements for higher-risk actions, and audit logs. The available controls and their exact behavior depend on the product and configuration; OpenAI describes these as part of its own deployment approach in Running Codex safely at OpenAI.
Quick Recap
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