AI Code Review Tools: Why Code Co-Pilots Need Supervision Like Interns

Modern AI code review tools have become essential as development teams increasingly rely on code co-pilots for software development. However, these AI assistants require the same level of oversight and guidance you’d provide to a team of interns.

Why AI Code Review Tools Are Essential for Code Co-Pilots

Code co-pilots work quite well… for tasks where you would consider hiring a mob of interns. Treat them like experienced developers, and you will be disappointed in their output. Want to use them to write a ton of unit tests? Sure. Should they architect a large system? Sorry to say, not a job for an intern. The “co” part of “co-pilot” is key. Just like you wouldn’t set an intern loose without supervision, AI code review tools ensure that co-pilots’ work is properly validated with a particularly critical eye.

Co-pilots provide lots of value, especially when used in situations where they can write code, and you then edit their output. This saves time, especially when the code is largely formulaic and tedious to write. However, without proper AI code review tools in place, the benefits can quickly turn into technical debt and security vulnerabilities.

Best Practices for Using AI Code Review Tools Effectively

Effective AI code review tools and co-pilots require similar management strategies to working with interns. I give similar advice to working with both.

Setting Clear Expectations and Breaking Down Tasks

When implementing AI code review tools, start by establishing clear workflows:

• Give clear expectations of work, and break it down into small tasks.
• Check their work after each task to ensure they understood correctly. If not, give clear instructions on where they went right and where there could have been some improvement. If things are wrong, you need to give redirection. Rephrase the request, break the work down even smaller, etc. It may be tempting to just fix it rather than giving feedback, but it’s worth investing the time.

Implementing Systematic Code Review Processes

• Point out similar patterns to follow. This is often called “multi-shot learning” for code co-pilots, but it’s actually closer to “multi-shot comprehension.” The easiest way to write new code is to follow an existing pattern.
• Modern AI code review tools excel at pattern recognition, helping identify when code deviates from established standards and best practices.
• If the desired output is at a higher level, e.g. a module or a repo, decompose the questions you ask. Ensure that the component pieces, e.g. files and classes, are understood and then summarize them. If you give an intern the whole repo, they’ll likely just be lost (as I explored in this previous blog post).

Pattern Recognition and Multi-Shot Learning

• Predefined test cases can help them understand what to write. Do not have them write both the code and the tests that evaluate the code (of course, this is good engineering practice, in general). It’s especially useful if you include edge cases which might have been overlooked by an engineer with less experience.
• Ask them to explain why they chose a certain approach. If this doesn’t make much sense, there’s a good chance that the code doesn’t either.

Common Pitfalls When Using AI Code Review Tools

Of course, you also want to ensure the tasks that you give them, whether human or model, fit their capabilities. Without proper AI code review tools, teams often make critical mistakes: Often, people give the LLM statistics and ask the LLM to summarize them in a way that requires repeating information in the inputs. Often, this is just kicking the tires, trying to understand whether they can do basic tasks for which you already know the answer. But there are better tools for this, and it’s a waste of time to use the wrong tools. You want to find the functions defined in a piece of code, use grep or a parser. If you then want to understand what those functions do, using an intern or an LLM is a fine choice. This seems like a statement of the obvious, but often people will use the LLM to do everything, and then become surprised when it misses something or makes it up. And, please, please don’t pass the LLM a list of functions and then ask it to spit it back out. It can only mess it up.

Questions need to be scaffolded, broken down into smaller pieces to ensure the question is understood and achieve consistency in the results. For example, instead of asking “Describe the purpose of this code,” you ask questions about the code structures, inputs, outputs, etc. Also, keep distractions to a minimum. Give them examples of what the answer looks like, including the output format you’d like.

Even with the best AI code review tools in place, LLMs will sometimes make mistakes. Ruthlessly checking the code—not just for correctness, but also for best practices—is vital. That’s the price you pay for the rest of the productivity. If, for a given task, fixing the messups is making the whole co-pilot experience not worth it, then accept your losses and move on. The key is to have it fail early on these tasks, to find out what it is good at. Ensure that they can’t mess things up too much. For example, if they are in the critical path, there is a policy to review their work (coming from someone who dropped a production table the first week of her internship).

How Flux Enhances Your AI Code Review Workflow

Here at Flux, we understand the critical need for comprehensive AI code review tools that go beyond basic syntax checking. While you might not want to put in the effort to break down the questions, we do. Our platform serves as an advanced AI code review tool that automatically evaluates code quality, security, and compliance across your entire codebase.

Flux integrates seamlessly with your existing development workflow, providing the automated code review capabilities that traditional AI code review tools often lack. But, also, like with interns, have patience. It’s still early days.

Ready to enhance your code review process? Learn how Flux’s AI code review tools can help your team maintain code quality while maximizing the benefits of AI co-pilots.