Delegate small, clearly specified coding tasks whose results you can inspect and test. Keep closer control of ambiguous, high-impact, security-sensitive, or learning-critical work. AI can help produce code, but you remain responsible for deciding whether it meets the requirement and is safe to use.
What makes a task suitable for AI pair programming?
Judge a task by its scope, clarity, risk, reviewability, and learning value—not by whether an AI assistant can generate something that looks plausible. A good first task is contained, has explicit acceptance criteria, and can be checked independently. GitHub’s task guidance offers practical examples, but these are starting points rather than universal rules.
- Scope: Is the change limited to a component or well-defined problem, or does it span systems and require broad judgment?
- Clarity: Can you state what should happen and how to tell when the change is correct?
- Risk: Could a mistake affect security, authentication, personal information, production, or an incident response?
- Reviewability: Can you understand the diff and verify behavior with tests or other checks?
- Learning value: Is doing the work yourself important because you are building understanding?
What should you delegate first?
Focused bug fixes
A bug with a reproducible failure and a clear expected behavior is a useful candidate. Provide the relevant context and ask for a narrowly scoped fix; then verify that the change addresses the failure without breaking nearby behavior.
Small UI and accessibility changes
A contained interface adjustment or accessibility improvement can be delegated when you can specify the desired outcome and inspect the result. Check both the code and the actual behavior against the requirement.
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Tests, documentation, and routine technical debt
Ask for additional test coverage, a documentation update, or a routine cleanup when the intended result is clear. These tasks still need review: tests can encode the wrong expectation, documentation can misstate behavior, and cleanup can alter it unexpectedly. GitHub includes these categories among its suggested task candidates in its guidance.
What should you keep under close human control?
Ambiguous requirements and broad refactors
If the goal is open-ended, or the change cuts across a repository or system, the assistant may make assumptions that are hard to spot in a large diff. Clarify the intended behavior and break the work into reviewable pieces before delegating implementation.
Rank #2
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Substantial business logic and legacy-system decisions
Keep ownership of decisions that depend on deep domain knowledge, undocumented behavior, or consequential trade-offs. An assistant may not know which existing quirks are essential, even when it can produce coherent-looking code.
Security-sensitive and production-critical work
Use particular care when changes touch security, personal information, authentication, production-critical systems, or incident response. GitHub specifically identifies these as sensitive areas in its task guidance. A generated suggestion is not a substitute for security review, incident judgment, or your organization’s approval process.
Rank #3
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Work whose purpose is to teach you
If you are learning a language, framework, or codebase, doing the reasoning yourself may be more valuable than delegating the implementation. You can still ask an assistant to explain a concept or review your approach, while retaining the problem-solving work that builds your understanding.
How to delegate without surrendering control
- State the goal and acceptance criteria. Describe expected behavior, relevant constraints, and what would count as a passing result.
- Give only the context needed to work well. Point to relevant files, conventions, and tests; avoid leaving important assumptions implicit.
- For multi-step work, request a plan or a small first change. Review that output before allowing the task to continue. GitHub recommends planning and iterative changes rather than immediately asking an agent to open a pull request in its task guidance.
- Inspect the diff against the original request. Check whether the change does what you asked, whether it introduced unrelated edits, and whether dependencies or edge cases were missed.
- Run the relevant checks. Use the project’s applicable tests and linters, then carry out code review and security checks appropriate to the change.
- Accept responsibility for the result. Do not merge or ship code simply because it compiles or reads convincingly.
GitHub cautions that generated code may appear valid while being incorrect or mismatched to developer intent. Its guidance says to use Copilot alongside testing, code review, security tools, and your own judgment: responsible-use guidance.
Rank #4
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Does AI pair programming make developers faster?
GitHub reported that participants using Copilot completed a programming task 55% faster than participants without Copilot in a controlled study published in 2022. That is a result for the study’s task and participants, not a forecast for every developer, codebase, or kind of work. The study report does not establish a universal productivity gain or a recommended percentage of work to delegate.
Review remains part of the work. An early Microsoft Research study described developers spending more time reviewing code than writing it and anticipated that AI suggestions would require additional assessment. The finding is a reminder to budget attention for verification, not a rule that every AI-assisted task takes longer: Microsoft Research study.
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