Hello, I'm Shrijith. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
Most engineers use LLMs by prompting them again and again—feeding file context directly each time. This approach works to some extent, but can be inefficient and lead to repetitive prompts.Let's explore a more efficient way to leverage LLMs for code review using a system like:
def review_code(code, lms_key):
# Code logic to interact with LLM using the provided key
return reviewed_text
nt. You’ll see a small productivity boost. But from my personal experience, it leaves most of the potential untapped.
In fact, I’d estimate you may be missing out on 80% of the real benefits LLMs can provide.
So what’s the alternative?
A Process, Not Just Prompts
Here’s the method I’ve been using:
- Iterate on a spec. Start by working with the LLM to co-create a spec in a markdown file. Refine it until it’s extremely detailed.
- Turn the spec into a checklist. Break the spec down into ordered, concrete tasks.
- Execute one step at a time. Work with the LLM to tick off each subtask until the entire checklist is complete.
Why This Works
The benefit is simple: the LLM never loses sight of the overall task context.
- You don’t have to re-explain things again and again.
- Even if the LLM forgets, you can just point it back to the spec or remind it of completed work.
- You stay in control of the process, using the spec and checklist as anchors.
Handling Reality Checks
Sometimes, during implementation, you’ll discover the spec is out of touch with reality—or the order of tasks in the checklist is wrong. That’s not a dead end.
You just update both with the LLM, then continue execution. The process adapts, and you keep moving forward.
Results
Using this method, I built a tough user management, license management, and RBAC system in just 2 days. Normally, this would have taken me at least half a month with traditional engineering—or at least a week with scattershot prompting.
Takeaway: LLMs aren’t just about writing clever prompts. They become far more powerful when you give them the same structure we rely on as engineers: specs and checklists.
Do you have any LLM use productivity tips of your own? Do share here for the benefit of the community.
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
⭐ Star it on GitHub:
HexmosTech
/
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
See It In Action
See git-lrc catch serious security issues such as leaked credentials, expensive cloud operations, and sensitive material in log statements
git-lrc-intro-60s.mp4
Why
- 🤖 AI agents silently break things. Code removed. Logic changed. Edge cases gone. You won't notice until production.
- 🔍 Catch it before it ships. AI-powered inline comments show you exactly what changed and what looks wrong.
- 🔁 Build a habit, ship better code. Regular review → fewer bugs → more robust code → better results in your team.
- 🔗 Why git? Git is universal. Every editor, every IDE, every AI…
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