{"id":13290,"date":"2026-01-15T18:22:57","date_gmt":"2026-01-15T18:22:57","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/"},"modified":"2026-03-17T13:38:58","modified_gmt":"2026-03-17T13:38:58","slug":"reviewing-code-generated-by-ai","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/reviewing-code-generated-by-ai\/","title":{"rendered":"Reviewing Code Generated by AI"},"content":{"rendered":"\n

With the increasing use of AI agents in software development, it’s becoming more common for parts of the codebase to be written with the help of automated tools<\/strong>. These agents can help accelerate repetitive tasks or suggest solutions, but it\u2019s still essential to carefully review everything that is generated.<\/p>\n\n\n\n

AI-generated code tends to follow generic patterns, often without considering the project\u2019s specific context or technical decision history. For this reason, several particular points should be evaluated during the review:<\/p>\n\n\n\n

Code organization<\/h2>\n\n\n\n

A very common issue in AI-generated code is the lack of structural organization. Since the agent doesn\u2019t have full knowledge of the project\u2019s architecture, it tends to group everything into a single file: controllers, services, models, and utility functions end up mixed.<\/p>\n\n\n\n

This approach might work for isolated examples, but in real-world projects with multiple contributors, it compromises clarity and maintainability.<\/p>\n\n\n\n

It\u2019s essential to verify that the implemented code is correctly positioned. Should the function be in another module? <\/strong>Does the logic belong to the layer where it was added? Are multiple responsibilities being handled in a single file? These questions help determine if the code respects separation of concerns and aligns with the project\u2019s architecture.<\/p>\n\n\n\n

Good organization facilitates testing, reuse, and future evolution. When everything is grouped without a clear structure, the risk of unnecessary coupling increases, and understanding the system becomes more difficult.<\/p>\n\n\n\n

That\u2019s why, when reviewing AI-generated code<\/strong>, ensuring everything is in the right place is just as important as verifying whether it works.<\/p>\n\n\n\n

Reimplementation of existing functions<\/h2>\n\n\n\n

Another frequent issue with AI-generated code is the reimplementation of existing functions within the project. This often happens when those utilities aren\u2019t well-documented or don\u2019t follow clear naming conventions. Since the agent can\u2019t see the full codebase, it ends up recreating logic from scratch even when a function with the same purpose already exists elsewhere.<\/p>\n\n\n\n

During review, it\u2019s essential to question whether the new function really needs to be created. Does it already exist elsewhere in the system? Could this have been solved with a simple import? Are there behavioral differences between the new code and the existing code?<\/p>\n\n\n\n

Rewriting something already available and tested not only increases code duplication but also makes maintenance harder, introduces inconsistencies, and may even lead to subtle bugs. Whenever possible, it\u2019s best to reuse what already exists, keeping logic centralized and consistent. The less duplication, the lower the risk and the greater the predictability of the system.<\/p>\n\n\n\n

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Read more: <\/strong>How to Integrate AI Into an App<\/a><\/p>\n<\/blockquote>\n\n\n\n

Invalid imports<\/h2>\n\n\n\n

A recurring behavior in AI-generated code is the attempt to import functions, classes, or modules that don\u2019t actually exist in the project. Often, the agent assumes that a component is part of the system based on common naming patterns and writes the import as if the code were available. The problem is that these elements might not exist, might be named differently, or reside in a different location with a different structure.<\/p>\n\n\n\n

During review, it\u2019s important to validate all import statements. Do the modules actually exist in the project? Are the function and class names accurate? Is the import aligned with the project\u2019s current organization?<\/p>\n\n\n\n

These types of errors might go unnoticed initially, especially in files that haven\u2019t been executed or tested yet. Still, they can cause significant problems later on when the code reaches production or requires maintenance.<\/p>\n\n\n\n

Ensuring that imports are accurate and contextually relevant is a fundamental, yet essential, step for\u00a0preventing<\/strong><\/span> future issues <\/strong>and maintaining the system\u2019s integrity.<\/p>\n\n\n\n

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Read more: <\/strong>Beyond \u201cVibe Coding\u201d: Engineering with AI and Cursor<\/a><\/p>\n<\/blockquote>\n\n\n\n

Misalignment with project standards<\/h2>\n\n\n\n

Even when AI-generated code is functional, it may be misaligned with the project\u2019s standards and conventions. It\u2019s common to see variable names that don\u2019t follow team conventions, missing type annotations in strongly typed projects, or structural inconsistencies such as business logic embedded in controllers or utility functions mixed with domain logic.<\/p>\n\n\n\n

This kind of misalignment affects readability<\/strong>, hinders maintenance, and contributes to a general feeling of disorganization. Sometimes, the AI even replicates poor practices already present in the codebase, reinforcing problems instead of correcting them.<\/p>\n\n\n\n

That\u2019s why the review process should verify whether function names, file names, and variable naming follow established conventions. It\u2019s also important to ensure that the architecture is being respected, that the code is placed in the correct layer, and with the right level of abstraction.<\/p>\n\n\n\n

Consistency is one of the key pillars <\/strong>for safely evolving a system. Even if the code works, misaligned implementations are harder to understand and more prone to bugs in future updates. Beyond verifying functionality, reviewers should ensure that the new code \u201cfits\u201d naturally within the existing system.<\/p>\n\n\n\n

Practical tips to avoid these issues<\/h2>\n\n\n\n

To reduce the most common problems found in AI-generated code, developers can adopt a few practical habits that improve reliability and alignment with the project\u2019s architecture:<\/p>\n\n\n\n