AI coding assistants can suggest a line, explain an unfamiliar function, or attempt changes across a project. Understanding how those features work helps you evaluate the product, set safe boundaries, and judge the code rather than the confidence of its explanation.
How Should You Review an AI Coding Assistant?
A coding assistant uses a language model and supplied context to generate likely useful code or explanations. An editor integration can supply nearby code and other selected project information; an agentic workflow may also search files, edit them, or run commands. The result is a proposed contribution, not proof of correctness, security, or compatibility with your application.
Looking for the main guide on this question? Read How AI Coding Assistants Use Context to Generate Code. This story takes a narrower angle.
From editor context to a proposed change
A typical interaction begins with a trigger: typing in an editor, requesting an explanation, or describing a task. The integration assembles input for a model, which may include instructions, nearby text, selected files, or retrieved snippets. The exact contents depend on the product, configuration, permissions, and mode.
The model generates output based on that input and patterns learned during training. This differs from traditional completion that primarily looks up symbols or follows language-server rules. A generated function can look plausible even when it refers to a nonexistent API or misunderstands a business requirement.
GitHub's responsible-use guidance for inline suggestions describes contextual code generation and its limitations. That is documentation for one implementation, not a guarantee that every assistant collects the same context or behaves identically.
After generation, an interface may show an inline suggestion, a chat answer, or an editable diff. Your evaluation should include this presentation layer. A clear diff makes review easier; a broad rewrite with little explanation may hide unrelated changes.
Do not assume the assistant sees the whole repository. Context selection is constrained, and relevant information can be omitted. Conversely, do not assume only the visible line is transmitted. Inspect documented data flows and administrative controls before using confidential code.
Distinguish completion, chat, and agents
Products often combine several modes under one name. Each mode creates a different review problem.
| Mode | Typical input and output | Main evaluation question |
|---|---|---|
| Inline completion | Nearby code becomes a suggested continuation | Does the suggestion fit the surrounding logic? |
| Code chat | A question and selected context become an explanation or snippet | Is the answer grounded in the actual project? |
| Edit assistance | A request becomes proposed file changes | Are all changes necessary and understandable? |
| Agent workflow | A goal leads to searches, edits, and tool calls | Are actions bounded, inspectable, and reversible? |
An agent can repeat a loop: inspect information, propose a step, call a tool, observe the result, and continue. Visual Studio Code's agents overview describes agent-based development workflows and associated tool use. Availability and controls vary, so evaluate the actual configuration offered to your team.
More autonomy is not inherently better. A completion tool may suit a small edit with clear local context. A multi-file migration might benefit from broader assistance, but it also demands a larger review surface and careful control over terminal commands.
Tool output is additional evidence, not unquestionable truth. A successful command can run the wrong test set, and a search can miss the relevant file. The human reviewer must understand what was checked.
Explain a task so it can be checked
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State the behavior, not just the implementation. Why: A request like “clean this up” leaves success undefined. How: Describe inputs, expected outputs, error cases, and compatibility requirements. Name anything that must not change.
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Supply the relevant context. Why: A model cannot reliably infer your internal conventions from a filename. How: Point to existing implementations, interfaces, and approved dependency versions. Share only information the tool is authorized to process.
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Request a limited change. Why: Smaller diffs are easier to inspect and reverse. How: Separate a bug fix from refactoring, formatting, and dependency upgrades. Ask for clarification before expanding scope.
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Specify verification. Why: “It works” is not a reproducible acceptance condition. How: Identify the tests or observable behavior needed, including failure cases. Ask the assistant to distinguish checks it actually ran from checks it merely recommends.
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Review the result independently. Why: A convincing explanation may repeat the same mistaken assumption as the code. How: Read the diff, compare it with the requirement, and run appropriate checks yourself or through a trusted development workflow.
These steps do not require elaborate prompts. A short, concrete request paired with the right files is usually easier to evaluate than a long description containing conflicting instructions.
A worked example: sorting records by date
Suppose a hypothetical team asks an assistant to “sort orders by date.” The instruction leaves several decisions open: ascending or descending order, which date field to use, how to interpret time zones, and what to do with missing values.
A plausible generated solution might sort date strings alphabetically. That can work for consistently formatted timestamps but fail for mixed display formats. Another solution might convert strings into dates while ignoring invalid values or mutating the original list unexpectedly.
A better request specifies: sort a copy of the input by creation time, newest first; treat valid timestamps as instants; preserve input order for equal timestamps; and put missing or invalid dates last. If the application's existing rules differ, those rules should take priority.
For a hypothetical five-record example, include two equal timestamps, one missing date, one invalid date, and one older valid date. The expected result should be written before accepting the generated implementation. That makes the acceptance test independent of how persuasive the assistant's explanation sounds.
Now inspect whether the code changes the original array, assumes a field always exists, or relies on an unavailable library. Check its behavior with an empty list and with the actual formats your application accepts. The lesson is not that sorting is unusually dangerous; it is that ordinary requests contain hidden requirements.
Evaluate quality beyond accepted suggestions
A high acceptance rate does not establish useful assistance. Developers can accept a suggestion and spend substantial time repairing it, or reject a useful suggestion because its interface is disruptive. Focus on completed, reviewed work.
Choose representative tasks such as understanding an existing function, fixing a small bug, adding an edge-case test, and making a contained change across files. Include the languages and frameworks your team actually maintains, rather than only common demonstration tasks.
Record whether the assistant uses real APIs, respects project conventions, and avoids unrelated edits. Track the effort needed to provide context, inspect the diff, and correct errors. Include failure examples in the evaluation notes so future users know the tool's boundaries.
Look closely at generated tests. Tests can assert the implementation's current behavior rather than the intended behavior, miss the important failure case, or mock away the very boundary that needs checking. Passing generated tests is useful evidence only when the tests themselves are sound.
For explanations, require references to relevant functions or files when practical. An assistant that confidently describes an imagined architecture can mislead a newcomer even if it never edits code.
Control data exposure and executable actions
Review the provider's policies and your organization's configuration for retention, training use, access control, and repository exclusions. Settings can vary across plans and deployments; do not generalize from a personal account to an enterprise arrangement.
Never place credentials in a prompt merely to make a tool call succeed. Use approved secret-handling mechanisms and restrict development environments to the minimum access needed. A request to inspect a repository should not imply permission to deploy it or contact production services.
Treat repository text, issue descriptions, and external pages as potentially untrusted input. Instructions embedded there may try to redirect an agent. Approval boundaries should depend on the action and its consequences, not on the fact that the instruction appeared in a project file.
Before enabling command execution, inspect the approval model and available isolation. Check how changes are recovered, what logs remain, and whether commands can access network resources or sensitive environment variables. Separate proposing a command from authorizing it.
Questions About Reviewing AI Coding Assistants
Does an assistant understand the whole codebase?
Not necessarily. It may use selected files, search results, or indexed snippets, and it can miss essential relationships. Ask it to identify the relevant code, then verify that its description matches the repository rather than assuming full awareness.
Can I use one without sending code to a remote service?
Some deployments support local models or other controlled arrangements, but the details are product-specific. Verify where inference runs and where context, telemetry, and logs go. A local editor extension does not by itself mean local processing.
Will generated code create licensing concerns?
That depends on the output, its similarity to existing material, the tool's features, and your organization's obligations. Inspect available reference or matching features and follow your existing legal review process. Do not treat generated code as automatically exempt from normal dependency and licensing checks.
Is a successful test run enough to merge a change?
No. Tests cover only the conditions they exercise. Review requirements, maintainability, security implications, and integration behavior as appropriate. Keep the same accountable approval process used for other contributions.
Check Every Suggested Code Change
Coding assistants are context-sensitive generation tools that can also become action-taking development workflows. Evaluate the quality of the resulting contribution and the controls around it, not the apparent intelligence of the interface.
Next steps: Compare deployment choices in general versus specialized AI, organize a trial with the evaluation framework, or explore the AI Reviews hub.
Sources
- GitHub — Responsible use of GitHub Copilot inline suggestions
- Visual Studio Code — Agents overview
This guide is general education, not professional security, legal, or purchasing advice.