A practical guide to writing specs that turn vague AI prompts into production-quality code — from functional requirements to edge cases, with real before-and-after examples.
Let me paint a picture you've probably lived.
You open Claude Code or OpenCode, type a vague prompt like "add a user dashboard with analytics," and hit enter. The agent spins up, writes a bunch of code — it even looks decent at first glance. Then you realize: the data model is wrong, the API endpoints don't match your existing patterns, there's no error handling, and the "analytics" is just a row of four hardcoded numbers.
You spend the next hour correcting, prompting, correcting again. You would have been faster writing it yourself.
Now imagine a different scenario. You spend 15 minutes writing a structured specification, paste it into the agent, and it produces exactly what you wanted — following your conventions, handling edge cases, wired into your existing auth and data layer. One shot.
