Here's exactly what made this possible: 4 documents that act as guardrails for your AI.

Document 1: Coding Guidelines - Every technology, pattern, and standard your project uses

Document 2: Database Structure - Complete schema design before you write any code

Document 3: Master Todo List - End-to-end breakdown of every feature and API

Document 4: Development Progress Log - Setup steps, decisions, and learnings

Plus a two-stage prompt strategy (plan-then-execute) that prevents code chaos. //

Here's the brutal truth: LLMs don't go off the rails because they're broken. They go off the rails because you don't build them any rails.

You treat your AI agent like an off-road, all-terrain vehicle, then wonder why it's going off the rails. You give it a blank canvas and expect a masterpiece.

Think about it this way - if you hired a talented but inexperienced developer, would you just say "build me an app" and walk away? Hell no. You'd give them:

• Coding standards
• Architecture guidelines
• Project requirements
• Regular check-ins

But somehow with AI, we think we can skip all that and just... prompt our way to success.

The solution isn't better prompts. It's better infrastructure.

You need to build the roads before you start driving.