Robert Ta argues that AI engineering teams should write detailed English specifications and test cases first, then have AI agents generate and refine code automatically. The leverage comes from humans defining what success looks like, not from writing code line by line.
The best and fastest AI engineering teams in Silicon Valley will have repos with literally zero handwritten code. The humans focus on the English and the specification. The agents write the code and implement, but the humans use their brains to figure out what is good and what is bad. That's spectrum development. Here's how it works with Cloud Code. Step 1, write what you want in plain English. Be precise. For example, here's a feature: password reset via email. User clicks forget password. The system will send a reset link valid for. The link then leads to a form requiring an 8+ character password with variation. Can't be an old password. Expired links then show an error with an option to request a new one. Every sentence there is a testable condition. Determinism. No ambiguity. Step 2, write the tests before the code. This isn't a new concept. Test-driven development has been a thing in the industry forever, and it's even more important in the age of AI because it's non-deterministic, these tools. So you gotta give it clear exit criteria. Step 3, Tell Claude Code, implement the spec, all tests must pass, then code review it and bring me the results. Claude Code then will read the spec that you've defined with what good is and the exit criteria so it knows when it's done. It'll write the implementation, it runs the tests, it reads the errors, it fixes the code, run the tests again, and it's in that loop just like a good engineer would because it has a good spec. You only get high leverage though when you put your brain on what is good, what is bad, and what's the exit criteria.
