Continuous Learning & Regression Prevention

The fastest way to destroy an engineering team’s trust in an AI tool is to force them to correct the same mistake twice.

If a senior engineer tells a junior engineer, “We don’t use the moment.js library here, we use date-fns,” the junior engineer learns. If an AI suggests using moment.js on Monday, gets rejected, and suggests it again on Wednesday, it becomes an annoyance.

The Static Prompt Problem

Most AI reviewers rely on static system prompts. They don’t have a mechanism to learn from the specific dynamics, preferences, and historical decisions of your engineering team.

The Standard: Dynamic Memory

A mature AI code reviewer must treat the Pull Request history as its primary training data for your specific repository.

1. Rejection Analysis: When a developer rejects an AI suggestion, the tool must analyze why it was rejected and update its internal context (or propose a new team rule) to never make that suggestion again.
2. Approval Analysis: When a developer approves a suggestion, the tool reinforces that pattern.
3. Regression Prevention: The AI should index past post-mortem reports and resolved high-severity bugs. If a developer introduces code that looks structurally similar to a bug that caused an outage six months ago, the AI must flag it instantly.

The AI should grow smarter alongside your team, effectively becoming a repository of institutional memory.