I found that once a project codebase gets complex, green tests are insufficient. Asking the LLM to review its own work was always “I got this”. So I tried the double-blind clinical protocol and christened it an unpronounacble DBASFR (Double-Blind Adversarial Split-Focus Review), you know, just like the pharma companies do, it’s the secret sauce. It sounds like an antibiotic DBASFR-cillin, but the protocol actually worked: Double-Blind: Strip the author model’s reasoning trace and excuse-making notes so the reviewer has zero idea who wrote it. Parallel reviewers sit in isolated rooms so they don’t form a union and agree on the same hallucination. Adversarial: Stop asking if it’s good. Tell the reviewer its only joy in life is proving the change broken with a concrete, reproducible failure trace. Split-Focus: Ditch the giant “review everything” prompt. Send one agent with extreme tunnel vision to hunt only state/lifecycle leaks, and another to police API contracts. It burns through tokens fast but less that if you proceed with broken code. I usually limit it to 2 simultaneous review agents. The improvement in catching structural blind spots was immediate. Apparently research benchmarks show the exact same thing: making reviewers blind, paranoid, and hostile pushes human-verified bug detection from ~48% to over 70%. Before I start pitching VCs on synthetic hostile consensus, has anyone else found their LLMs only do real review work once you treat them like hostile lab rats? submitted by /u/GianniMariani
Originally posted by u/GianniMariani on r/ArtificialInteligence