Good programmers use AI as a pair partner by making the goal explicit, asking it to surface uncertainties, and giving it small, reviewable tasks—not by accepting a stream of generated code on trust. A behavior-focused specification, a separate technical plan, and tests create checkpoints where the programmer can correct assumptions before they spread.
What makes AI a useful pair partner?
A coding assistant can draft plans, break work into tasks, suggest implementations, and help examine tests. But it cannot reliably infer requirements that were never stated. The programmer remains responsible for deciding what to build, setting constraints, reviewing consequential changes, and validating the result.
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GitHub’s framing is that coding agents should be treated “more like literal-minded pair programmers” than search engines. That is a useful reminder about the interaction: the assistant responds to the context and instructions it receives, so vague direction leaves room for mistaken assumptions. It is not evidence that a particular workflow guarantees better outcomes. GitHub’s introduction to spec-driven development describes a process built around a specification, a plan, tasks, implementation, and review.
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Use the lightest process that makes the important decisions visible. A concise prompt and immediate validation may be enough for a clear, low-risk change. More structure is useful when requirements are ambiguous, a wrong assumption would be costly, architecture or operational constraints matter, or the work can be divided into independently testable pieces. Writing and maintaining artifacts also has a cost, so do not create ceremony for its own sake.
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- Keep it lightweight: the change is narrow, behavior is obvious, and a quick test can catch a mistake.
- Write a specification and plan: users, outcomes, edge cases, integrations, or constraints need clarification.
- Use explicit task and review checkpoints: the change has several parts or a mistaken decision could affect later work.
A 2026 practitioner overview describes a spectrum of specification rigor: spec-first, spec-anchored, and spec-as-source. These are categories for thinking about process weight, not measured performance results. The paper’s arXiv record is dated January 30, 2026.
How to work through an AI-assisted feature
1. Clarify the goal before asking for code
Start with the person who needs the feature and the problem they are trying to solve. Ask the assistant to help identify missing decisions rather than immediately drafting an implementation. Useful prompts include:
- Who will use this, and what are they trying to accomplish?
- What should success look like from the user’s point of view?
- What is explicitly out of scope?
- Which edge cases, permissions, or failure states could change the behavior?
- What information is still unknown, and which decisions must a person make?
These questions are a practical way to uncover ambiguity, not a canonical checklist prescribed by GitHub. GitHub’s specification phase emphasizes users, problems, journeys, and successful outcomes. Stop questioning when there is enough clarity to proceed; record unresolved questions instead of letting the assistant silently decide them.
2. Write a behavior-focused specification
Describe what the system should do and how someone will recognize that it works. Keep this separate from the implementation details: “A registered user can request a password reset and receives a time-limited link” describes behavior; naming a framework or database belongs in the technical plan.
Include the important normal path, relevant error or edge cases, and observable acceptance criteria. Keep the document revisable: when a new discovery changes the desired behavior, update the specification rather than treating the first draft as an unchangeable contract. GitHub describes the spec as a living artifact and shared source of truth.
3. Plan the technical approach separately
Give the assistant the context that should shape implementation: the existing stack, architecture, repository conventions, integrations, compliance requirements, and performance constraints. Ask it to list assumptions and, when a decision is genuinely open, compare plausible alternatives with trade-offs. The programmer chooses; the assistant’s proposal is not authorization to change project direction.
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4. Decompose the work into reviewable tasks
Break the plan into changes small enough to inspect and test in isolation. “Build authentication” is too broad to review as one unit. A specific registration endpoint with email validation is a more bounded example of the kind of task breakdown shown in GitHub’s workflow. Each task should make clear which part of the behavior it supports and what evidence will show it is complete.
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For each task, provide the assistant with the relevant specification and plan context, then request a bounded change. Review the result against the intended behavior before moving on. This is where pair-like collaboration is most useful: the assistant can propose an artifact, while the programmer checks whether it fits the feature and the surrounding project.
6. Validate, then revise the right artifact
Run the tests relevant to the change and inspect whether the implementation meets the acceptance criteria. Also revisit the plan if repository realities make it impractical, and the specification if new information changes the desired behavior. OpenAI’s prompt engineering guidance recommends clarity about role and workflow, and testing and validation for coding tasks. A passing test suite is useful evidence, not a substitute for checking that the tests cover the required behavior.
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What should the programmer keep control of?
Human oversight should be explicit, especially where a change affects users, security, data, or system behavior beyond the task’s clear scope. Set boundaries for what the assistant may modify, require review before consequential actions, and make important decisions yourself. The OpenAI Model Spec states that humanity should remain in control and that autonomy should stay within a clear, mutually understood scope.
- Set the intended behavior and constraints rather than delegating them by omission.
- Review changes that affect architecture, permissions, data handling, or external integrations.
- Validate implementation with tests and inspection before relying on it.
- Return to the specification when the desired outcome changes; do not let an outdated plan overrule it.
Common ways AI pairing goes wrong
Starting with an underspecified request
A prompt such as “add login” leaves critical questions unanswered: supported sign-in methods, recovery behavior, validation, and security requirements. Ask enough to expose decisions, then write down the answers that matter.
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Letting the plan become the goal
A technically plausible plan can still solve the wrong problem. Keep user behavior and success criteria in the specification, and check each implementation task against them.
Asking questions without reaching a decision
Socratic inquiry is a technique for surfacing assumptions, not a reason to prolong the conversation. When the remaining uncertainty is non-blocking, record it and proceed within a clear scope. When it affects behavior or risk, resolve it first.
Making a change too large to review
Broad tasks make it harder to isolate defects and understand why a result differs from the intended behavior. Split work at meaningful test or review boundaries, then validate each increment.
Treating generated code or a passing test as proof
Neither output is self-validating. Check that the change matches the specification and that tests exercise the behavior that matters. If the requirement changed, revise the specification and tests instead of merely adjusting code to satisfy an obsolete expectation.
Is spec-driven development a guarantee of better results?
No quantitative productivity, defect-rate, or outcome claim is established by the sources cited here. The workflow is a practical way to make assumptions, decisions, and validation points visible; its usefulness depends on the task and on whether the programmer actually reviews the artifacts. For a small, obvious change, a full specification may be unnecessary. For complex or constrained work, an explicit chain from behavior to plan to tasks to tests gives both programmer and assistant a clearer basis for action.
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