3.2. Validating and Operating AI Tools
💡 First Principle: Validation is not a one-time gate at the end — it's a continuous stance woven through how you operate the tool. Because output is probabilistic, every suggestion is provisional until a human (and your tests/review) confirm it.
Why this matters: "validate AI output" is arguably the single most-tested idea on the GH-300, and it appears in multiple domains. This section makes it concrete: what validation actually involves, and what "operating responsibly" looks like day to day.
The mental model: Copilot's output is a draft from a fast junior colleague. You'd never merge a junior's PR unread; the same care applies to AI suggestions, scaled to the risk of the change.
⚠️ Common Misconception: "Validating AI output means checking that the code compiles." Compilation is necessary but nowhere near sufficient — validation also covers correctness against requirements, security, performance, edge cases, and licensing.
