[formrecap]

The concept of blind spots in same-model auditing is sound, but I'm skeptical that just adding "orthogonal" to a prompt solves it. Which axis was the model using before? Which should it use next? Without knowing that, you're just hoping for variety.

What actually works in my experience is two things:

First, prompting with specific personas. "You are a security auditor looking for multi-tenant isolation failures" unlocks genuinely different reasoning from "review this code." The lens matters more than the word "orthogonal" — it gives the model a concrete perspective to reason from.

Second — and I think this gets overlooked — anchoring AI review in deterministic tooling. Semgrep, ESLint, dependency audits. These tools have been catching bugs reliably for years. A model asked to "review this code" will always find something — they're trained to be helpful, I've never had one say "nope, it's perfect." But pairing that with deterministic tools gives you consistency and catches the things models miss by construction.

It's not really new. It's just working with AI agents the way you'd work with another team member — while knowing their limitations (like regurgitating semantically similar ideas when asked the same question twice).