🤖 AI-Assisted Code Review Pipeline

Code review bottlenecks are one of the most common sources of developer frustration on growing teams. Senior engineers become the throughput constraint; PRs sit waiting for architectural feedback; the feedback that arrives is inconsistent depending on who reviews it. An AI-assisted first-pass review doesn't replace the human reviewer — it makes the human review faster and more focused.

The idea is a GitHub Actions workflow that triggers on pull request open or update. It uses an LLM (Claude, in my case) with a carefully crafted system prompt that encodes the team's architectural principles and common anti-patterns. The model reviews the diff, identifies design-level concerns — boundary violations, missing error handling, inappropriate coupling, schema changes without migration strategy — and posts a structured review comment before any human touches the PR.

The human reviewer then comes in knowing that surface-level mechanical issues have already been flagged, and they can focus their energy on intent, domain correctness, and the concerns the model missed. Over time, the prompts evolve based on what the team finds valuable versus noisy. The system becomes a living encoding of the team's quality standards.

One extension I find interesting: tracking which AI-flagged issues get accepted versus dismissed over time. That feedback loop could be used to refine the prompt and also gives you data on common recurring anti-patterns in your codebase — useful for targeted training sessions or architectural documentation.