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Use the time to prepare for and verify the change: clarify the intended behavior, inspect the code and tests it will touch, then review and test the generated diff before it is merged. AI can speed up parts of coding, but generated code is a draft—not a substitute for understanding, engineering judgment, or human accountability.
What should you do while the assistant is generating code?
Do not wait passively or approve a large patch on sight. Use the generation time to build the context needed to judge whether the result is correct.
- Make the request testable. State the behavior you want, relevant constraints, edge cases, and what success looks like. If those are unclear, resolve them before asking the assistant to implement the change.
- Inspect the surrounding system. Read the relevant functions, interfaces, project conventions, tests, and dependency configuration. Identify assumptions about data, permissions, error handling, and compatibility that the generated code must preserve.
- Plan how you will verify it. Find the tests that cover the behavior, note missing cases, and decide which additional tests or static and security checks are appropriate. For a risky change, consider how it can be divided into smaller, reviewable pieces.
- Think about exposure and tool fit. Choose autocomplete, chat, or agentic generation according to the task and repository context. Check whether local or hosted execution fits your privacy and security requirements, and whether the tool makes its changes easy to inspect.
How do you review AI-generated code?
Read the diff in small pieces
Review the actual changes, not just the assistant’s explanation. For each change, ask whether it is necessary, understandable, consistent with the project’s conventions, and compatible with the requested behavior. Watch for unrelated edits, missing error handling, incorrect assumptions about interfaces, and tests that do not exercise the important edge cases.
Verify dependencies and security assumptions
Check any newly suggested package or version against a trusted package source rather than accepting it because it looks plausible. Examine how the change handles inputs, secrets, permissions, and sensitive data. UK Government guidance recommends checking dependency versions against trusted sources and warns against relying on nondeterministic prompt responses without extensive testing. Read the UK Government’s guidance for developers.
Run the checks that match the risk
Run relevant tests and appropriate static or security checks; add tests where the existing suite does not establish the expected behavior. A passing test suite is useful evidence, not proof that every requirement is met. Check the output against the intended behavior and project constraints as well as against automated results.
Where does human accountability belong?
The person shipping the change remains responsible for understanding it. UK Government guidance puts the rule plainly: “You should only commit code changes that you understand.” It also says merges to the main branch need human peer review and must follow organizational policy. UK Government developer guidance.
Keep changes small enough to review, preserve branch protections, and involve peers for important merges. If you cannot explain what a generated change does or why it is safe, do not merge it yet: investigate, revise, or remove it.
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What does the evidence say about AI coding benefits?
Benefits are plausible, but results depend on the task, study design, and organization. They should not be treated as a guarantee that AI makes every developer faster or every codebase better.
Rank #3
A bounded code-quality study
In a 2024 GitHub study, 202 developers with at least five years of experience completed a specific web-server API exercise. The Copilot group was reported as 53.2% more likely to pass all ten unit tests—a relative likelihood, not a 53.2 percentage-point increase. The study also reported statistically significant differences of 3.62% in readability, 2.94% in reliability, 2.47% in maintainability, and 4.16% in conciseness. These findings apply to that study setup, not automatically to other languages, developers, or repositories. GitHub’s study and methodology.
A government workplace trial
In a UK Government Digital Service trial that ran from November 2024 to February 2025, users estimated average savings of 56 minutes per working day. The report cautions that task estimates could overlap and optimism bias may inflate reported savings; it also notes missing telemetry for one month. Copilot telemetry in the trial showed an average acceptance rate of 15.8% for suggested code lines, while 58% of survey respondents said they would not want to return to pre-trial working conditions. Each measure captures a different thing: reported time savings, accepted lines, and respondent sentiment are not interchangeable measures of productivity. Read the trial findings and caveats.
Team context and delivery quality
DORA’s 2025 report describes AI as an amplifier of an organization’s existing strengths and weaknesses, not a fix for weak engineering practices. Its 2024 report found productivity benefits alongside reduced delivery stability and throughput, reinforcing the importance of small batches, robust testing, and focus on user needs. DORA’s 2025 report and DORA’s 2024 report.
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Review is part of the work, not an optional afterthought. Organizations should evaluate their AI tools regularly and provide enough time and people to review generated code for quality and security. eu-LISA’s report summary emphasizes those needs but does not establish a quantitative result that can be applied to every team. eu-LISA’s report summary.
Best Value
Measure whether the assistant improves the whole delivery process, rather than relying only on typing speed or the number of accepted lines. The right workflow depends on the task’s risk, language, repository context, privacy constraints, and how readily the output can be tested and reviewed.
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