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I had a pull request with 800 lines of code. I wanted a quick check for bugs and style. I feared sending private code to a public AI. My legal team would hate it. I needed a private way to review code.
I tried several paths.
Local models were too random. They guessed line numbers. LangChain felt heavy and slow. Enterprise plans cost too much for small teams.
I found a better way. I wrote a thin Python module. It uses an OpenAI-compatible API. I forced the AI to output JSON.
This changed everything.
Here is what I learned:
- Prompting matters more than model size.
- JSON mode is mandatory. It stops AI rambling.
- Privacy does not always mean local. Signed agreements work too.
- AI misses deep architectural flaws. Use it for style and simple bugs.
Keep your tools simple. I wasted days on complex frameworks when a basic API call worked best.
How do you handle AI code reviews? Do you use local models or a trusted provider?
Optional learning community: https://t.me/GyaanSetuAi