ClaudeCode or Cursor like a magic wand, only to end up with a codebase that is a fragmented mess of contradictory logic once the project exceeds 1,000 lines.
The problem isn't the AI; it's the lack of a structural blueprint. When you just "vibe" with an LLM agent, you're essentially letting a junior developer write your code while you act as a rubber stamp. Eventually, you hit a bug that the AI can't fix because it has hallucinated a dependency or created a circular reference that it can no longer "see" in its context window.
To avoid this, I've shifted my AI workflow to a "Specs-First" approach. Instead of asking the AI to "build a feature," I provide a strict technical contract.
My AI Workflow for Stability #
-
Define the Schema First: I never let the AI generate a database schema on the fly. I write the SQL or Prisma schema manually and feed it to the AI as a source of truth.
-
Modular Prompting: Instead of one giant prompt, I break the feature into atomic tasks.
-
Explicit Constraint Mapping: I use a
.cursorrules
file or a system prompt to enforce strict patterns.
For example, I force my agent to follow this logic for every new component:
// Constraint: No inline styles, use Tailwind.
// Constraint: All state management must happen in the parent container.
// Constraint: Export as a named function, not default.
export function UserProfileCard({ user }) {
return (
<div className="p-4 border rounded-lg">
<h2 className="text-lg font-bold">{user.name}</h2>
</div>
);
}
- Manual Audit Cycles: Every 3-4 AI-generated iterations, I stop and perform a manual deep dive. I check for redundant functions and dead code that the AI often leaves behind.
If you're treating prompt engineering as a way to avoid reading documentation, you're just building technical debt at 10x speed. The goal is to use the LLM as a high-speed implementer, not the lead architect.
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