Delegate small, clearly defined coding tasks whose results you can check; keep close control of work that is ambiguous, high-impact, security-sensitive, or important to your own learning. AI can help produce code, but you remain responsible for whether it meets the requirement, works in context, and is safe to ship.
How to decide what to delegate
Assess each task by its scope, clarity, risk, reviewability, and learning value. These factors matter more than a fixed rule about what percentage of coding to hand to an AI assistant.
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- Scope: Is this a contained change, or a broad refactor that affects many parts of the system?
- Clarity: Can you state the required behavior and how to verify it?
- Risk: Would a mistake be easy to reverse, or could it expose data, undermine authentication, or disrupt production?
- Reviewability: Can you understand the proposed diff and independently check its behavior?
- Learning value: Do you need to solve this yourself to build an understanding of the code or domain?
GitHub’s Copilot task guidance recommends beginning with bounded work and taking extra care with security, personal information, authentication, production-critical changes, and incident response. It is product guidance, not a universal rule for every assistant or codebase: GitHub’s task guidance.
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Good tasks to hand off first
Start with work that has a narrow purpose, relevant context, and a result you can readily inspect. GitHub’s examples include:
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- A focused bug fix with a reproducible failure.
- A small UI change.
- Additional test coverage.
- Documentation updates.
- An accessibility improvement.
- Routine technical debt.
Describe the expected behavior, point to relevant files or patterns, and give acceptance criteria. For example, instead of asking for a general improvement to a form, specify the field behavior to change, the error state users should see, and the tests that should pass. Clear boundaries make it easier to spot when the implementation has drifted from the request.
Delegate larger work in checkpoints
A multi-step task can still be a reasonable delegation if you break it into inspectable pieces. Rather than asking an agent to make a broad change all at once, ask it to outline an approach or implement a small first step. Review the resulting diff and tests before expanding the scope. GitHub’s guidance recommends planning and iterative changes over immediately asking an agent to open a pull request: Copilot task best practices.
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- Define the outcome. State what should change and what must remain unchanged.
- Request a plan or first increment. Keep the initial change small enough to evaluate.
- Inspect the diff. Check whether each change serves the requirement and fits the surrounding code.
- Run relevant checks. Use the project’s tests, linters, and other appropriate validation.
- Continue only after review. Refine the next step based on what the first increment revealed.
Keep close human ownership of risky or unclear work
Do not treat plausible-looking code as proof that a task was understood or solved correctly. Keep yourself closely involved when requirements are open-ended, changes cross many systems, or correctness depends on domain knowledge that is difficult to express in a prompt.
- Ambiguous requirements: Resolve what the product should do before delegating implementation.
- Broad refactors: Split the work into smaller changes with explicit boundaries and checks.
- Substantial business logic: Verify rules and edge cases against authoritative product requirements.
- Security and authentication: Review the design and implementation carefully; use the security checks appropriate to the system.
- Personal information, production-critical changes, and incident response: Maintain direct oversight because mistakes can have high consequences.
- Learning tasks: If your goal is to understand a system or practice a technique, doing the core work yourself may be more valuable than delegating it.
How to review AI-generated code before accepting it
Compare the change with the original requirement, not just with the assistant’s explanation. GitHub cautions that generated code can appear valid while being incorrect or failing to reflect developer intent: Responsible use of Copilot code completion.
Rank #3
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- Check the behavior. Does the code satisfy each acceptance criterion, including relevant edge cases?
- Read the whole diff. Look for unrelated edits, changed assumptions, dependencies, and effects on nearby code.
- Run relevant tests and linters. Passing checks provide evidence, but do not by themselves establish that the behavior is right.
- Use code review and security checks. Apply the same review practices and tools you would use for human-written changes.
- Accept responsibility for the result. Do not merge code you cannot explain or validate.
GitHub’s product guidance puts the boundary plainly: “Of course, you should always use GitHub Copilot together with good testing and code review practices and security tools, as well as your own judgment.” GitHub’s responsible-use guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What productivity evidence does—and does not—show
In 2022, GitHub reported that participants using Copilot completed a programming task 55% faster than participants without Copilot in its controlled study: GitHub’s study report. That result applies to the study task and participants; it is not a promised gain for every developer, task, or codebase.
Rank #4
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- Gaming Keyboard: All keys on this aula s99 wireless keyboard support macro customization, which allows you to record and edit macros to program a series of complex actions into a key, useful in very real-time games for amateur gamers.If you have very strict requirements for game response speed, it is recommended that you purchase a mechanical keyboard priced at $50 or more, which is more suitable for professional gamers.The aula s99 pc keyboard is compatible with Windows XP/7/8/10, Mac, Android and iOS. Please NOTE: this product is a membrane keyboard not mechanical keyboard and this doesn't support hot-swapping
Microsoft Research’s early Copilot study described developers spending more time reviewing code than writing it, pointing to the continuing importance of assessment as assistants suggest more code: Microsoft Research’s study. Neither finding establishes a universal delegation threshold or a numeric share of work that developers should hand to AI.
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