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Senior developers treat AI coding assistants as fast collaborators, not authorities: they give them a narrow task, relevant context, and clear acceptance criteria, then inspect and test every change before it becomes part of the system. That approach can help with work such as drafting tests, explaining code, or handling repetitive edits, but it does not transfer responsibility for correctness, security, or maintainability.
What should you delegate to an AI coding assistant?
Start with a task whose output you can verify. GitHub’s guidance describes use cases such as writing tests, generating repetitive code, debugging syntax, explaining code, and creating regular expressions; it also describes chat as useful for code questions, larger drafts that will be iterated, and planning. These are vendor-described use cases, not guarantees that a suggestion will be correct or save time (GitHub Copilot guidance; Visual Studio Code chat guidance).
A practical test for delegation is whether you can state the desired result and independently check it. Ask for a test case, a small refactor, an explanation of a function, or a draft implementation of a well-defined behavior. Keep architectural decisions, ambiguous requirements, and high-impact changes under close human direction.
How do you give it enough context?
Describe the job in terms of the codebase and the behavior you need, not only the desired code. Include the relevant files or symbols, the expected inputs and outputs, a representative example, and constraints such as supported versions, conventions, or performance requirements. Microsoft’s prompt guidance recommends supplying relevant context and examples; GitHub likewise advises clear, specific prompts and breaking complex work into smaller tasks (Visual Studio Code prompt-crafting guidance; GitHub Copilot guidance).
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- State the outcome: Explain what behavior should change and what must remain unchanged.
- Point to the local patterns: Name the files, functions, interfaces, or tests the assistant should follow.
- Make acceptance criteria observable: Specify expected results, error cases, and relevant edge conditions.
- Limit scope: Ask for one reviewable change rather than an open-ended rewrite.
- Keep context current: Include the latest relevant code and omit unrelated material.
Do not paste credentials, customer data, or other sensitive material unless your organization’s policy explicitly permits that tool and use. Tool settings and organizational rules determine what data may be entered; a prompt cannot override them.
A prompt pattern
For example: “In parseConfig, reject a missing port and return the existing validation error type. Preserve the current behavior for valid configuration. Follow the error-handling pattern in loadSettings. Add tests for a missing port, a valid port, and a non-numeric value. Do not change public interfaces.” This gives the assistant a bounded task, a code reference, constraints, and checks without asking it to make broad assumptions.
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When should you ask for a plan before code?
If the request could be interpreted in more than one way, ask the assistant to summarize its understanding, list assumptions, and outline a small implementation plan before editing. This makes disagreements about scope or behavior easier to catch while the change is still a proposal. You can also ask it to identify likely edge cases or alternatives.
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How should you review AI-generated code?
Review the change as the engineer who will own it. Read every changed line, understand the resulting behavior, and check whether it fits the project’s architecture and conventions. A plausible-looking implementation may misunderstand intent, introduce unnecessary dependencies, or handle only the example in the prompt.
- Compare the diff with the requested behavior and acceptance criteria.
- Look for unintended changes to public interfaces, data handling, error paths, and dependencies.
- Check input validation, boundary cases, and whether failures are handled consistently.
- Confirm that names, abstractions, and complexity are appropriate for future maintainers.
- Ask for an explanation of a section you do not understand, then verify that explanation in the code.
If you cannot explain what a change does or why it is safe, do not approve it merely because it compiles or the assistant says it is finished.
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What checks should run before merging?
Use the project’s normal verification process, independent of the assistant’s confidence. Run relevant tests and, where the project uses them, linting, type checking, code scanning, and security testing. Add or adapt tests for the behavior you requested, including failure and edge cases. AI-drafted tests can speed up test authoring, but they may encode the same mistaken assumption as the implementation and may not cover the scenarios that matter.
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Review dependencies and potential intellectual-property concerns under your organization’s process. Confirm that no secrets or sensitive information have entered code, logs, prompts, or generated files. Passing automated checks is necessary evidence for many changes, but it does not replace understanding the diff or deciding whether the change belongs in the system.
How do you preserve accountability?
Keep ordinary engineering records and approval steps in place: document the change, review it, run the required checks, and retain traceability according to team policy. The UK Home Office engineering standard, updated 20 March 2026, says: “AI‑assisted outputs MUST be reviewed and approved by a human before reaching production” (UK Home Office engineering standard). That is a requirement for that organization’s standard, not a universal rule for every employer or jurisdiction; your own approved-tool, data-handling, and review policies govern your work.
What does the evidence say about productivity?
A 2024 arXiv study used 27 semi-structured interviews and analyzed 190 relevant Reddit posts and comments to explore software professionals’ security practices around AI coding tools (arXiv study). Those figures describe the study’s material; they are not measurements of productivity, defect rates, or the experience of all developers. They should not be used to claim that AI universally makes developers faster or produces better code.
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