Quality erosion after AI integratio

The "AI-induced rot" is real: you start with a clean codebase, let Cursor or Copilot handle the boilerplate, and six months later, you're staring at a mountain of "hallucinated patterns" where the same logic is implemented four different ways across the project. I've noticed a dangerous trend where developers stop auditing the architecture and start only auditing the syntax. If the code runs and the tests pass, it gets merged, but the conceptual integrity of the system slowly dissolves.

The biggest culprit is the "Context Drift." When you use @Codebase or similar features, the AI often suggests a solution based on a snippet of code it found in a legacy folder that you forgot to delete. It mimics your existing patterns—even the bad ones—creating a feedback loop of mediocrity.

To stop this erosion, I've shifted my workflow from "Generation Mode" to "Constraint Mode." Instead of letting the AI decide how to implement a feature, I provide a strict architectural blueprint in a .cursorrules file (or a project-specific prompt) that explicitly forbids certain patterns.

For example, I now force the AI to follow a strict functional approach for state management to prevent it from scattering useEffect hooks everywhere in my React projects:

# Architecture Rules
- No business logic inside components; move all logic to custom hooks.
- Use Zod for all API response validation; never trust a raw JSON return.
- Prefer composition over inheritance.
- If a function exceeds 20 lines, split it into smaller, pure utility functions.
- Avoid using 'any' types; if a type is unknown, use 'unknown' and narrow it.

Another major gotcha is the "Refactor Loop." You ask the AI to optimize a function, it changes the variable names and adds a few comments, and you accept the change because it looks "cleaner." But then you realize it subtly changed the edge-case handling. I've stopped using "Accept All" for large diffs. Now, I use a "Verification Prompt" before committing any AI refactor:

Analyze the diff between the current version and the proposed change. 
Identify any changes in logic, edge-case handling, or time complexity. 
List these changes as a bulleted list of "Behavioral Changes" before I merge.

The productivity gain comes from the AI, but the quality must come from the human. If you aren't spending at least 30% of your time questioning the AI's structural choices, you're just automating the creation of technical debt.

My current setup for maintaining quality:

• Strict .cursorrules: Defines the "Source of Truth" for coding style so the AI doesn't guess.
• Manual Type-First Development: I write the TypeScript interfaces myself first, then let the AI fill in the implementation. This ensures the data flow is what I intended.
• Atomic Commits: I commit every single AI-generated change separately. If a bug appears three days later, I can pinpoint exactly which "AI hallucination" caused the regression.
• Test-Driven Prompting: I write the test case first, then feed the failing test to the AI. This prevents the AI from writing code that "looks" right but fails in production.

The goal isn't to write code faster; it's to maintain a system that is still maintainable two years from now. AI is a great junior dev, but it's a terrible architect. Stop letting it lead the design.