Rethinking Code Review in the Era of AI
AI has promised to help developers move faster without sacrificing quality, and on many fronts, it has. Today, most developers use AI tools in their daily workflows and report that it helps them work faster and increase code output. In fact, our developer survey shows nearly 70% of developers feel that AI agents have increased their productivity. But speed is outpacing scrutiny, and this is introducing a new kind of risk that is harder to detect and introduces many scenarios where it is more expensive to fix than speed justifies.
The issue isn’t that AI produces “messy” code. It’s actually the opposite. AI-generated code is often readable, structured, and follows familiar patterns. At a glance, it looks production-ready. However, surface quality can be misleading; that code that doesn’t appear “messy” can still cause a mess. The real gaps tend to sit underneath, in the assumptions the code is built on.
Quality Signals Are Harder to Spot
AI doesn’t fail the same way humans do. When an inexperienced or rushed developer makes a mistake, it’s usually clear to the reviewer: an edge case is missed, a function is incomplete, or the logic is off. When AI-generated code fails, it’s rarely because of syntax, but because of context.The confidence AI shows when it is wrong about a historical fact is the same confidence it presents in the code it shares.
Without a full understanding of the system it’s contributing to, the model fills in gaps based on patterns that don’t always match the specifics of a given environment. That can lead to code that lacks context on data structures, misinterprets how an API behaves, or applies generic security measures that don’t hold up in real-world scenarios or lack the context engineers have about the system.
Developers are making these new challenges known, reporting that their top frustration is dealing with AI-generated solutions that are almost correct but not quite, and second most cited frustration is the time it takes to debug those solutions. We see huge gains on the front end of workflows from rapid prototyping, but then we pay for it in later cycles, double and triple checking work, or debugging issues that slip through.
Findings from Anthropic’s recent education report reveal another layer to this reality: among those using AI tools for code generation, users were less likely to identify missing context or question the model’s reasoning compared to those using generative AI for other purposes.
The result is flawed code that slips through early-stage reviews and surfaces later, when it’s much harder to fix since it is often foundational to subsequent code additions.
Review Alone Isn’t Enough to Catch AI Slop
If the root problem is missing context, then the most effective place to address it is at the prompting stage before the code is even generated.
In practice, however, many prompts are still too high-level. They describe the desired outcome but often lack the details that define how to get there. The model must fill in those gaps on its own without the mountain of context engineers have, which is where misalignment can happen. That misalignment can be between engineers, requirements, or even other AI tools.
Further, prompting should be treated as an iterative process. Asking the model to explain its approach or call out potential weaknesses can surface issues before the code is ever sent for review. This shifts prompting from a single request to a back-and-forth exchange where the developer questions assumptions before accepting AI outputs. This human-in-the-loop approach ensures developer expertise is always layered on top of AI-generated code, not replaced by it, reducing the risk of subtle errors that make it into production.
Because different engineers will always have different prompting habits, introducing a shared structure can also help. Teams don’t need heavy processes, but they do benefit from having common expectations around what good prompting looks like and how assumptions should be validated. Even simple guidelines can reduce repeat issues and make outcomes more predictable.
A New Approach to Validation
AI hasn’t eliminated complexity in software development — it’s just shifted where it sits. Teams that once spent most of their time writing code now must spend that time validating it. Without adapting the development process to account for new AI coding tools, problem discovery will get pushed further downstream, where costs rise and debugging becomes more complex, without taking advantage of the time savings in other steps.
In AI-assisted programming, better outputs start with better inputs. Prompting is now a core part of the engineering process, and good code hinges on providing the model with clear context based on human-validated company knowledge from the outset. Getting that part right has a direct impact on the quality of what follows.
Rather than focusing solely on reviewing completed code, engineers now play a more active role in ensuring that the right context is embedded from the start.
When done intentionally and with care, speed and quality no longer have to remain at odds. Teams that successfully shift validation earlier in their workflow will spend less time debugging late-stage issues and actually reap the benefits of faster coding cycles.
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