Teams waste months building features that miss the mark. The fix? Train AI coding assistants with detailed specs and custom instructions instead of using them as autocomplete.
The story
I’ve watched too many teams burn months building features that miss the mark – or worse, ship code nobody can maintain.
The fix? Spec-driven development paired with AI coding assistants you actually train.
Here’s what most people get wrong: they treat tools like GitHub Copilot or Cursor as glorified autocomplete. But when you feed these assistants detailed specs, coding standards, and custom instructions, they become extensions of your team’s brain.
I do this with every Creative Robot project. [First month’s free, by the way – because I’d rather you see results than hear promises.]
Start with a clear spec: business logic, data models, acceptance criteria. Then encode it into files the AI reads – a `.github/copilot-instructions.md` or Cursor rules file. Include your naming conventions, preferred libraries, testing frameworks, architectural patterns.
Now the assistant generates code that actually fits your system. It comments complex logic. It keeps docs current. And with proper configuration, these tools can even execute builds and tests – safely, with appropriate project controls.
The difference is night and day. A terse prompt like “tail recursion” yields code tailored to your environment, not generic Stack Overflow copy-paste.
But here’s the catch: you still review everything. AI can perpetuate bad patterns if you let it. Document what it generates, refine its outputs, and keep your team aligned on the logic.
I don’t just consult on this stuff – I build it, deploy it, and coach your team hands-on until they own the process.
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