Three Rules for Developing with an AI Assistant: From Requirements to Maintainable Code

Developers have long been wary of handing their code to an AI assistant. The fear is understandable: an assistant can violate requirements, break legacy code or write an unmaintainable solution. But these fears can be addressed, and what it takes is rules.

The problem: AI without requirements

The first problem is that AI invents requirements instead of following them. You give the assistant the task "add authorization," and it adds not what you described in the spec but something of its own. Fixing it afterward takes longer than writing it yourself.

The second problem is backward compatibility. Changes break existing code: a bug in one place, and the whole legacy codebase stops working. AI does not see this unless you tell it explicitly.

The third problem is quality gates. AI can write code that works but is impossible to maintain: poor variable names, giant functions, no tests.

Three rules for managing AI

Rule one: pin down the requirements. Not a verbal briefing, but a document. AI works with text, so:

• Write a spec with input/output examples
• Show the existing code (how things are structured in the project)
• Specify anti-patterns ("don't do it this way, because...")
• Attach a list of tests that must pass

Rule two: ensure backward compatibility. Before submission, the code must pass:

• Regression tests (old functionality still works)
• Type checking (TypeScript or Mypy)
• Linters (a consistent style)
• A build check (the production build does not break)

Rule three: code review quality. Don't rely on AI alone. We use:

• Automated checks (lint, type, build)
• Manual review by a second developer
• Smoke tests before deployment

How it works in practice

When these three rules are in place, AI becomes an effective helper. The assistant follows the spec precisely, its code passes all checks, and backward compatibility is guaranteed. The developer can focus on architecture and delegate the routine work.

The result: you ship features faster, with fewer bugs, and the code is easier to maintain. And this is not theory. It works on real, complex products where every change is critical.

We aren't afraid of AI, because we know the rules of the game.

What this means

AI in development is not a threat if you follow three rules: requirements, compatibility, quality gates. Then the assistant works like a junior developer with good discipline.