Okay, I know you may be skeptical: other guides have promised painless only to reveal that their solution requires some hyper-specific tech stack or a paid developer tool. I won't do that to you.
This guide provides a straightforward and flexible template for that you can apply to your engineering team. The only requirement is that your app code is .
You can test a TypeScript workflow, Java workflow, Python workflow, PHP, Ruby or even some wacky web stack you invented. And it doesn't matter if you're developing on Windows, Linux, or Mac. Best of all, you don't have to perform convoluted configuration or install software beyond a .
I've been in engineering for the last 15 years, and have a bad reputation. We've all witnessed or lived through horror stories where sometimes it feels like every previous line gets torn to shreds.
So, what can you do differently? How can you make reviewing your code painless so that even the biggest nitpick on your team has nothing but praise?
After participating in code reviews for a decade, taking code reviews less personally is the single biggest thing you can do to improve your code. Why? Because all software is iterative. Even "perfect" code will eventually become outdated. Instead of thinking of it like a graded assignment, think of it as a part of the process.
This tutorial uses free, open-source tools. You'll need to have a GitHub account to help you make your code reviews more pleasant and valuable.
The term "code review" can refer to various activities, from simply reading code over your teammate's shoulder to a 10-person meeting where you dissect code line by line. I use the term to refer to a formal and written process, but not so heavyweight as a series of in-person code inspection meetings.
In a project where you work on a repository with other developers, after you complete your work, you commit, push, and create a pull request on the VCS, most likely using Git commands. Then, everyone reviews the pull request to determine whether it's okay to use. If so, they approve it, and that code gets used in the project.
Code Reviews are a tool for knowledge transfer. They help make devs more efficient when doing maintenance on a part of the system they didn't write.
When you review a pull request, it's an opportunity to iron out issues before they become technical debt.
Code reviews can also be a good setting for mentoring junior developers.
Now, let's discuss what is not the purpose of a code review:
Nitpicking on style issues - settle for one style and use formatters or AI tools to enforce it. Just keep in mind that there are many things that an AI tool cannot check. Code reviews are an excellent place to ensure the code is sufficiently documented or self-documenting.
Do you want to know how you can check this? Return to the code you wrote 6-12 months ago and try to understand what it was written to do.
If you understand it quickly, that means it's readable, and the code review was done properly and in a helpful manner.
Despite their importance, many devs don't like doing code reviews - in part because they can be challenging, especially if you're not following best practices.
Here are some pain points I've observed during my years of participating in code reviews:
These pain points often bottleneck our development velocity. But recent advances in AI-assisted code review tools have started addressing these common friction points in our PR workflows.
Let's explore how AI-powered tools, along with some best practices, can address these review challenges and optimize your development workflow.
While AI hasn't replaced human code reviews, it is a powerful force multiplier in the review process.
Here's how: AI code reviews excel as a preliminary screening tool, catching common issues before human reviewers see the code. This becomes especially valuable in open-source projects where maintainer bandwidth is limited.
I recently started using AI code reviews on a case-by-case basis for my projects.
AI tools improve my existing workflows, reduce failure rates by detecting logic errors early on, and boost productivity. So I've added it to my CI/CD pipelines. It doesn't have to be perfect at detecting logic errors, as long as its false positive rate is very low (ideally as close to 0 as possible).
Most importantly, AI reviews respect the golden rule of 'value your reviewer's time' by handling routine checks. This allows human reviewers to focus on architecture, business logic, and complex edge cases.
This approach positions AI as a complementary tool that augments rather than replaces human expertise in the code review process.
When reviewing code, try to prioritise what matters most using the Code Review Pyramid. This is a framework that helps you focus your attention where it creates the most value.
Think of it like building a house -- start with the foundation before worrying about paint colours.
The pyramid has five layers, from most critical (bottom) to least critical (top):
Remember: if you want to catch issues/bugs, there are more appropriate processes for that. That is why we have automated testing, canary releases, testing environments, and so on.
In my personal opinion, using code reviews as a bug catching tool is somewhat of an anti-pattern where you're compensating for a development process that may be lacking some key steps/processes.
There is no general rule in engineering for code reviews, as what you'll need to focus on depends on many factors. You can and should set up the process according to your company standards and way of working as a team.
Here are some factors you'll need to think about before setting up a code review process:
As an example, at my work we have a very simple rule: all code changes must be reviewed by at least one developer before a merge or a commit to the trunk.
Code reviews need a systematic approach, but maintaining consistency across every PR is challenging. It's useful to let computers handle repetitive checks (style, formatting) while humans focus on what matters most: architecture and logic. This balanced approach makes reviews both thorough and sustainable.
Take a look at this example. It shows how we can optimize our process by intelligently delegating tasks between humans and automated tools. The diagram below illustrates a typical code style review workflow, comparing manual human review steps against automated tooling.
The diagram shows a real problem we all face in code reviews. See the left side? That's we humans doing manual formatting checks: finding weird spaces, fixing indents, writing comments about it... pretty tedious stuff. But check out the right side: that's where tools like just fix these formatting issues automatically.
No meetings, no back-and-forth - just done. That's why I started using , which is a dev tool that caught my attention recently.
The CodeRabbit docs describe the tool pretty effectively, so I'll just leave this here:
CodeRabbit is an AI-powered code reviewer that delivers context-aware feedback on pull requests within minutes, reducing the time and effort needed for manual code reviews. It provides a fresh perspective and catches issues that are often missed, enhancing the overall review quality. - from the CodeRabbit docs
Let me walk you through a real example. When you submit a PR, CodeRabbit:
I first discovered last month while I was searching for something else on GitHub. I accidentally came across it and I was surprised by how many people are already using it.
I instantly signed up because I was looking for exactly such a solution which could help me and my team out with our reviews.
I read through the CodeRabbit docs and was very impressed.
Getting started using it is pretty much a plug and play process.
In the next section, we'll go through the quick steps you can follow to enable CodeRabbit using an example repo.
Next, add CodeRabbit to some of your public GitHub repositories.
Now, CodeRabbit is fully integrated and ready to do code reviews on your selected repo.
Yes: it's that simple and fast. And in my opinion, it's one of the main reasons the tool is so useful.
Here are some sample PRs for you to check out:
Everyone's code needs reviewing. Just because someone is the most senior person on the team does not mean that their code doesn't need to be reviewed.
In this article, I talked about code reviews along with some common pain points. I then showed you how you can leverage CodeRabbit to iterate quickly through your code reviews and focus more on business.
In this article I talked about basic intro to CodeRabbit, because that was my use case with my blog.
For more advanced functionality, check out the official CodeRabbit docs or read their blog.
I hope you found it helpful learning how to use AI tools for code reviews.
If you like my writing, these are some of my other most recent articles.
