Enhancing the role of the developer with the responsibility of reviewing AI-generated code is a transformative step for developers. You become a critical gatekeeper for the quality, reliability, and maintainability of code produced by advanced AI tools like GitHub Copilot. While the volume of code reviews may increase, so does the opportunity to raise the bar for your team’s output. This post explores how reviewing AI-generated code can make you more productive and effective and provides practical tips for navigating common review challenges.

How Reviewing AI-Generated Code Boosts Productivity

Data from recent development teams shows that integrating AI code generation can increase feature delivery speed by 20–40%. However, this gain is only sustainable if code reviewers ensure the produced code meets the highest standards. By adopting consistent review practices, developers spend less time debugging and refactoring later, resulting in a net productivity gain even with the extra reviews required. Moreover, reviewers report a deeper understanding of the codebase and technologies as they regularly encounter new patterns and solutions presented by AI.

Key Areas for Reviewing AI-Generated Code

When faced with code from AI assistants, code reviewers should pay special attention to the following areas:

1. API Design & Interface Architecture

Interface Abstraction: AI often introduces unnecessary abstraction layers; scrutinize interfaces for simplicity and directness.

@copilot TokenCredential is already abstract, we don't need an interface for it.

Method Naming: Naming conventions can be inconsistent (e.g., WithHostPort vs WithBrowserPort); ensure adherence to project standards.

Public vs Internal APIs: AI may expose more methods as public than needed—be deliberate about API surface.

Extension Method Patterns: Confirm builder extensions follow established conventions.

2. Testing & Testability

Unit Test Coverage: AI-generated methods may lack comprehensive tests for new public methods—insist on full coverage.

@copilot add unit tests for GetOrCreateResourceAsync

Test Organization: Prefer snapshot testing (e.g., Verify) over generic assertions, which are common in AI-generated tests.

Concrete Assertions: Review for tests that assert specific values, not just general outcomes.

Preserve Existing Tests: Guard against unnecessary changes to existing tests when integrating new code.

3. File Organization & Architecture

Auto-generated Files: AI may inadvertently modify auto-generated API surface files (/api/.cs)—review for accidental changes.

Layer Separation: Confirm code is placed within the correct architectural context (Infrastructure vs Publishing).

Namespace Organization: Check that new classes and interfaces are organized in the appropriate assemblies.

@copilot Move the tests for BicepUtilities to a BicepUtilitiesTest class

4. Error Handling & Edge Cases

Null Checking: Validate that null-checking patterns are applied consistently.

@copilot This should never be null.

Exception Handling: Ensure the use of proper exception types and handling strategies; AI might use generic exceptions.

Edge Case Coverage: Be thorough in considering error scenarios and defensive programming, especially as AI may overlook rare cases.

5. Configuration & Resource Management

Resource Lifecycle: Inspect resource creation, configuration, and cleanup, as AI code may neglect disposal patterns.

@copilot We should see if the DockerComposeEnvironmentResource already has a dashboard resource and this should noop if it does.

Configuration Patterns: Confirm adherence to established callbacks and resource configuration approaches.

Environment-Specific Logic: Ensure correct behavior in different contexts (e.g., publish vs run modes).

6. Code Quality & Standards

Documentation: AI-generated code often lacks comprehensive XML documentation for public APIs.

Code Style: Watch for formatting and style inconsistencies that AI can introduce.

Performance Considerations: Critically assess the performance implications of AI-generated designs.

Key Insights for Reviewing AI-Generated Pull Requests

• Iterative Refinement: Expect Copilot PRs to go through more rounds of feedback and incremental edits than human-authored code.
• Architectural Guidance: Provide strong architectural support to ensure new features mesh with existing patterns and conventions.
• Standards Enforcement: Maintain rigorous standards, as AI often defaults to generic practices unless explicitly guided.
• Quality Focus: Devote attention to maintainability and test coverage; AI may solve the immediate task but miss long-term concerns.
• Incremental Changes: Encourage smaller, focused pull requests to simplify review and integration.

Conclusion: Elevate Your Impact as an AI Code Reviewer

Embracing the role of reviewing AI-generated code allows you to steer your team’s adoption of new technologies toward success. By applying deliberate review strategies, enforcing standards, and guiding iterative refinement, you ensure that the promise of AI productivity is realized without compromising quality. Step up as a reviewer, help make every AI-generated contribution robust and maintainable, and lead the way for excellence in .NET development.