Trust protocol | Master AI Automation in 4 hours Master AI Automation in 4 hours Course About Ayush Modules Sample chapter Toolbox The Microcap Minute Classroom / Module 11: Safety, Privacy & Nonsense Detection / Chapter 4 Trust protocol Watch first, then read. Same lesson, your pace. What you will learn – The claim-by-claim verification workflow – What “high stakes” means operationally – Calibrated trust: neither paranoia nor faith From vibe to protocol Modules 1, 7 and 8 each touched verification. This chapter assembles the pieces into one repeatable routine, the difference between knowing knives are sharp and owning a kitchen protocol. The TRUST check: T, Tag claims. Ask the AI itself: “List every factual claim in your answer as numbered bullet points.” Vague paragraphs become countable units. R, Rank stakes. Which claims would embarrass/cost you if wrong? Those get checked; colour-theory trivia doesn’t. U, Unearth primaries. For ranked claims: find the original source (Module 8’s rule), the report, not the recap. S, Second opinion. Cross-model check (Kimi ↔ Claude ↔ Qwen): agreement isn’t proof, disagreement is a stop sign. T, Track it. Keep links beside claims in your notes; uncheckable work isn’t shippable. Runs in minutes for typical homework/work output; scales down to instinct for low-stakes chat. High-stakes = human-owned Some categories earn mandatory full protocol regardless of how confident the AI sounds: Health/medication questions → verify with medical sources/professionals Money/tax/legal → official documents or professionals; AI drafts, humans decide Academic submissions → citation-checked or not submitted Anything published under your name → your name carries it Note the pattern: AI may assist all of these; accountability never transfers. “The AI said so” has convinced no teacher, doctor or judge yet. Calibration, both directions The failure modes live at extremes. Under-trusters waste the leverage this course built (re-doing everything manually). Over-trusters ship hallucinated numbers into exams and family groups (Chapter 3’s distribution problem). The professional stance: default draft-status, escalate verification with stakes, and let track-record teach you which tasks/models deserve head-trust. Keep a light log of caught errors for a month, your personal hallucination map beats anyone else’s warnings. Try it yourself Take one substantial AI answer you actually plan to use this week. Run full TRUST: tag its claims (count them), rank top-3 by stakes, unearth primaries for those three, second-opinion in another model, file links. Then write your personal escalation ladder (“low stakes → X, medium → Y, high → Z”) in learn/trust-protocol.md . Key takeaways – TRUST: tag claims → rank stakes → unearth primaries → second model → track links. – High stakes (health/money/law/published work) = full checks, always; assistance yes, accountability no. – Calibrate between paranoia and faith using your own error log. Download the exercise sheet (PDF) Module workbook (PDF) ← Prev: Seeing isn’t believing Module 11 index → Classroom / Module 11: Safety, Privacy & Nonsense Detection / Chapter 4 Trust protocol What you will learn – The claim-by-claim verification workflow – What “high stakes” means operationally – Calibrated trust: neither paranoia nor faith From vibe to protocol Modules 1, 7 and 8 each touched verification. This chapter assembles the pieces into one repeatable routine, the difference between knowing knives are sharp and owning a kitchen protocol. The TRUST check: T, Tag claims. Ask the AI itself: “List every factual claim in your answer as numbered bullet points.” Vague paragraphs become countable units. R, Rank stakes. Which claims would embarrass/cost you if wrong? Those get checked; colour-theory trivia doesn’t. U, Unearth primaries. For ranked claims: find the original source (Module 8’s rule), the report, not the recap. S, Second opinion. Cross-model check (Kimi ↔ Claude ↔ Qwen): agreement isn’t proof,
Trust protocol
Written by
in