Smarter, Not Harder: Effective Code Review with Gemini

Code review is a critical pillar of software development, but often can be the biggest bottleneck in the development cycle. Pending pull requests can slow down velocity, create inconsistencies, and burn out your engineers who spend hours on routine checks. Why not automate the first pass and free up your team to focus on what really matters?

In my experience, moving this first pass to AI is rapidly becoming a mandatory shift. It’s about optimizing resource allocation, there’s simply no compelling technical or business reason to continue doing this manually. The conversation has shifted entirely to choosing the right model or ‘Assistant’ to handle your initial code review pass.

In this blog post I share my experience of using Gemini for code reviews for the last month’s.

The Challenge: The Code Review Bottleneck

Every developer knows the pain of a stalled PR. The back-and-forth on minor style issues, the wait for an experienced eye to spot potential bugs, and the challenge of enforcing team-wide best practices can slow you down.

Research from DORA’s Impact of Generative AI in Software Development report advises that organizations, “Double-down on fast high-quality feedback, like code reviews and automated testing, using gen AI as appropriate.”

It’s important to emphasize that no code review process, manual or automated, is perfect. Quality always stems from the right combination of people, processes, and culture, with or without AI.

Naturally, there’s community chatter about negative experiences, which some might cite as a reason not to adopt AI for code review. My perspective aligns with a spot on community comment: The value is in how you engage. Don’t seek perfection, seek improvement.

Use AI to your advantage, in most scenarios human-in-the-loop is required, some recommendations sometimes don’t make sense in your context, take in what makes sense, discard what doesn’t, and then add a human to review it when your process demands it, that way it will certainly save you time! Keep reading if you want to know how it worked for me…

The Solution: An AI Teammate in Your Pull Requests

Gemini Code Assist integrates directly into your GitHub workflow, acting as an automated reviewer on every pull request. The moment a new PR is created, the gemini-code-assist[bot] gets to work.

Instead of waiting for a human, you get near-instant feedback. The bot provides a high-level summary of the changes to orient reviewers and then dives deep, leaving actionable, inline comments on the specific lines of code that need attention. This allows human reviewers to skip the mundane checks and focus immediately on the architectural and logical soundness of the changes.

Setup is remarkably fast, taking me less than 5 minutes to integrate Gemini Code Assist across our organization’s repositories.

How to effectively use it

Gemini Code Assist isn’t just a simple linter; it’s an interactive partner in the development process. Its functionality can be broken down into three core areas.

The Automated First Pass

Within minutes of a PR being opened, Gemini delivers a comprehensive two-part review:

• PR Summary: A comment in the main conversation tab gives a high-level overview of the changes, highlights significant modifications, and provides a detailed changelog.

Given the core strength of LLMs is summarization, the PR summary is a time saver, even if you create a PR template to force developers to fill some important context, quite often it’s not descriptive enough, so this can be used as a combined context.

• Inline Findings: The bot posts detailed comments directly on the modified lines of code. Each comment includes a severity rating (Critical, High, Medium, Low) and often provides a ready-to-commit code suggestion, dramatically speeding up fixes.

Circling back to the post intro, after using it for a few months, the inline findings have proven to be the biggest time-saver, and with the right customization, it’s a bit like the Pareto principle, not in a way that just 20% of the findings are useful, up to 70% were either taken in or useful context, but within 20% some critical bugs were spotted that otherwise could have been overlooked.

This is where Gemini truly shines: acting as an additional, tireless set of eyes to analyze code context, detect complex patterns, and flag possible critical issues. This capability alone validates the shift to AI-assisted review.

Let me go over some samples over using it for hundreds of PR’s:

1 — referencing external docs to spot a bug

within a SQL query, a line with a PARTITION BY was missing a column, something that could be easily forgotten and go unnoticed.

2 — referencing your team’s specific standards through a style guide

A hardcoded string is simple to catch, but it’s ability to detect that and automatically catch violations under your specific standards, this is the perfect example of the type of check that ensures your team focuses on architectural complexity and business logic.

3 — A good style guide is gold context for your AI

the better your style guide and practices defined in your code base, more accurate the AI reviews and catching these types of issues will be.

Customization is Key

As I alluded to in the examples, the most powerful feature is the ability to align the AI with your team’s specific standards. By creating a .gemini/ directory in your repo, you can add two key files:

• config.yaml: This file lets you control the AI’s behavior. You can set a minimum severity for comments to avoid noise or tell the AI to ignore certain files and directories (like generated code or vendor libraries).
• styleguide.md: This is a simple Markdown file where you can describe your team’s unique coding standards in plain English. For example, you can specify rules like, “Use the log/slog package for structured logging instead of fmt.Println,” or “API response objects must always be defined in the pkg/types directory.” The AI will then enforce these custom rules, effectively codifying your team’s tribal knowledge.

In the google-gemini cookbook repo, you can find good examples of both config and styleguide files.

Interactive Controls

It’s important to note that you aren’t limited to the bot’s initial review. Any contributor can manually trigger the AI using simple slash commands in a comment:

• /gemini review: Triggers a full re-review of the PR.
• /gemini summary: Requests a new summary of the changes.

You can also have a conversation with the AI by tagging @gemini-code-assist in any comment to ask clarifying questions or request alternative implementations.

The Bottom Line: Pricing and Value

A huge advantage for Gemini is its incredibly generous free tier:

Most of times you just go into the free quota, for more enterprise like setups, you have the per user pricing here, which is certainly worth the value, when the free quota is not enough.

Conclusion

To be clear: Gemini Code Assist is not a replacement for human expertise. It’s a time saver. By automating the essential but repetitive first-pass review, catching common errors, enforcing consistency, and providing crystal-clear summaries, Gemini frees your engineers. This reclaimed bandwidth can be dedicated to the complex architectural and business logic challenges where human insight remains irreplaceable.

For any team prioritizing both accelerated delivery and sustainable code quality, adopting Gemini Code Assist is the most logical and low-risk step forward.

I challenge you to begin with the generous free tier today. See the impact on your team’s flow, and embrace your most efficient new AI teammate.