How to Use LLMs for Code Review Automation

Description and use

An LLM‑based code review bot integrated into CI/CD quickly flags style and security issues, letting developers focus on architecture.

Problem

Slow, manual code review cycles delay merges and expose hidden anti‑patterns.

Outcome

The workflow delivers a first‑pass code review that flags low‑level issues, enabling human reviewers to focus on higher‑level concerns and accelerating merge times.

Workflow

• Install openai and gitpython dependencies
• Create a plain‑text file with team rules (e.g., rate limiting, error handling, no secrets)
• Write a Python script that: 1) extracts the git diff, 2) loads rules, 3) constructs a prompt with context and diff, 4) calls the LLM with a JSON response format, 5) prints or posts the review results
• Integrate the script into a CI/CD pipeline (GitHub Actions or GitLab CI) so it runs automatically on PR creation or push

Evidence

LLMs perform consistently better when they understand the code’s intended functionality

Source

The most effective pattern is a hybrid process combining human involvement and LLMs

Source