Code arriving quickly in an editor is easy to mistake for progress. The more useful question is whether it makes the next decision clearer and leaves something that can be checked. That is the standard I would use when deciding where GitHub Copilot belongs in a development workflow.
This is a practical way to assess the tool, rather than a claim about a measured productivity gain. Different tasks expose different strengths, and time spent accepting a suggestion is only a small part of the time spent owning it.
Start with work whose result is easy to recognise
A small, repetitive transformation is a reasonable place to start. For example, take a known response shape and map a few fields into a view model. The input, output and missing-value behaviour can all be written down before the implementation is suggested.
Copilot’s inline suggestions documentation describes assistance with code while also emphasising limitations and the need for review and testing. A plausible completion is a candidate implementation. It does not establish that the surrounding requirement has been understood.
Other useful candidates include drafting a small test fixture, suggesting an explanation of unfamiliar code, or proposing the repetitive part of an adapter. In each case, keep a way to check the answer against the repository, the dependency documentation or a stated example.
Give the task a boundary
A bounded request is easier to review than “improve this file”. Describe the allowed change, the behaviour that must stay the same and the cases that matter. The following is an illustrative request, not a record of an actual project or a guaranteed result.
Suggest a pure function that maps these records to labels.
Do not change the input records or add a dependency.
Preserve an empty string; use "Untitled" only for null or missing titles.
Show examples for each case before suggesting the implementation.The empty-string detail matters. A short expression using a truthiness fallback could appear tidy while silently changing that requirement. The benefit of the prompt is that it makes the decision explicit enough to challenge, whether the proposed code comes from a person or an assistant.
Do not let the implementation define its own tests
A generated function and generated tests can agree with each other while both misunderstanding the task. Write the expected behaviour from the requirement first. For the example above, include a title of an empty string, a missing title, a null title and an ordinary title.
Then inspect whether the tests would fail for the obvious wrong implementation. If every test is just another version of the happy path, a green result says very little about the distinction that mattered. Tests should help a reviewer understand what was decided, not simply increase a count.
Run the project’s relevant checks and inspect the actual change. Confirm that imported functions exist in the installed version, that errors are handled at the right layer, and that a tidy refactor has not expanded the scope. For permissions, data handling or external calls, review the surrounding design as well as the individual lines.
Count the work that follows the suggestion
Keep a simple record when trialling the workflow: the task, the accepted change, the review time and any correction needed later. Include suggestions that were discarded. Without that, the memorable moments are likely to be the fast completions rather than the slow misunderstandings.
A useful suggestion should leave the developer better able to explain the result. If reading it creates more uncertainty than writing a smaller version would have done, reduce the task or stop using the suggestion. There is no obligation to keep code because it arrived quickly.
The practical value is support for well-bounded work, with the developer still deciding what correct means. That fits the broader preference for smaller, understandable interfaces: there should be less uncertainty left for the next person who opens the project.