AI coding tools are causing a seismic shift in the way software is written. Developers are no longer writing software line-by-line; instead, they’re using large language models (LLMs) to generate everything from small edits to entire files in seconds. This faster, AI-powered iteration loop, sometimes referred to as “vibe coding,” can boost productivity, but it also introduces new risks: logic bugs, security gaps, and technical debt can creep in if engineers don’t review and understand the generated code.
The solution isn’t to slow down or stop using AI. Instead, teams need better ways to review AI-generated code. In fact, tools like Graphite are helping teams turn vibe coding into something more robust: responsible AI-assisted development, where code is still written quickly, but passes through high-quality, human-in-the-loop feedback cycles before it ships. In this post, we’ll explore the challenges of reviewing AI-generated code and show how AI tools can help teams keep quality high, even as output scales.
What is vibe coding?
Vibe coding is a new development technique where devs rely heavily on AI to generate code, often without fully understanding the entire scope of what the model produces. Rather than writing every line of code themselves, devs work more intuitively—they go off of the “vibes.”
However, just because you are using AI in the development process doesn’t necessarily mean you are vibe coding. Plenty of teams use AI to write code faster—but as one developer puts it, “When I talk about vibe coding I mean building software with an LLM without reviewing the code it writes.” This approach can be great for rapid prototyping, but generating production code with vibes alone introduces a host of new challenges for developers in the rest of the software development lifecycle (SDLC).
The challenges vibe coding creates
As more code is written, it puts more pressure on the “outer loop” of the development cycle. Reviewing, testing, and merging quickly become the bottleneck to shipping faster.
With the sheer volume of potentially unchecked vibe code entering the outer loop, teams face issues that today’s manual code review workflows weren't designed to handle, such as:
Review bottlenecks: Reviews take longer when code is unfamiliar or AI-generated because devs have to spend extra time understanding logic they didn't write.
Exponential complexity: Teams of ten now face challenges of scale that previously would have been faced by a team of 100 due to the volume of output.
Knowledge gaps: Developers may not fully understand the code they're shipping, making it harder to debug issues or modify functionality later.
Technical debt accumulation: AI-generated code might work but could be inefficient, poorly structured, or use outdated patterns that create long-term maintenance problems.
Testing blind spots: Teams might not know what edge cases to test for since they didn't think through all the implementation details themselves.
This leads to an interesting question: If we’re increasingly using AI for generating code, can AI help ease the burden of reviewing all that code?
Reviewing AI-generated code with AI
In practice, introducing AI to the code review process isn’t just an effective solution—it’s quickly becoming a necessity for creating high-quality software with AI. Without pairing AI code generation tools with an equally powerful AI reviewer, teams risk technical debt spiraling out of control. Fortunately, AI code reviewers can now analyze each pull request in seconds catching everything a human reviewer would notice—and more. For instance, Diamond, Graphite’s AI code review companion, can automatically detect and flag:
Subtle logic errors.
Security vulnerabilities.
Performance issues.
Code style or quality issues specific to your org.
Diamond scans your PRs and provides you with instant, actionable feedback that you can commit with a single click.
In fact, 30-35% of all actionable code review comments at organizations using Diamond come from the AI tool. This is huge for vibe coding teams who need a first layer of quality control on AI-generated code—and now human reviewers can spend more of their time thinking through high-level functionality and architecture decisions rather than hunting for bugs in code they didn't write. Adopting AI in the code review process leads to better code, faster iterations, and ultimately, higher-quality software that keeps both your teams and end users happy.
Steps for standing up AI powered reviews within your teams
Choose an AI review tool
Chances are, if your teams are vibe coding, you’re already comfortable with them using AI-powered tools. Now, it becomes a question of which tool makes sense for your team. Prioritize looking for features like seamless integration with your current tools (GitHub, GitLab, etc.), customization capabilities, and actionable feedback features. For example, Diamond allows teams to define custom rules aligned with their internal coding standards, so teams can enforce specific practices such as avoiding certain patterns or enforcing naming conventions.
Establish clear roles
It’s important to define the roles of AI versus human reviewers for efficiency in your review process. AI tools excel at automated checks, catching syntax errors, enforcing style consistency, and identifying security vulnerabilities or performance issues. Meanwhile, human reviewers can focus on broader architectural decisions, contextual business requirements, and creative problem-solving. A clear workflow could be: AI initially reviews code, developers incorporate that feedback, and human reviewers then address high-level concerns. This way, you can leverage the strengths of both AI and your development team.
Monitor, measure, adapt
To effectively implement AI code reviews, you need to monitor and adapt your processes based on actionable metrics. Diamond provides insights into your team's productivity gains from AI code review, including the number of pull requests reviewed, issues identified, and the rate at which AI-suggested changes are accepted or dismissed. By analyzing this data, teams can assess Diamond’s effectiveness, identify areas for improvement, and make informed decisions on how to adapt their review processes.
Keep reviews on pace with AI code generation
As AI continues to accelerate software development, implementing an AI/human hybrid code review process is essential to keeping your code correct, performant, and secure. Don’t fall behind the wave; get started with Diamond to make sure your teams maintain speed and innovation without sacrificing quality or incurring technical debt. If you're curious about how our engineers are approaching AI-powered development and reviews, check out this article, too.
