Engineering Series · Course 2 of 6
// AI will never say "I don't know." Your system must.

Trust Engineering.
The math that makes AI trustworthy.

Self-consistency sampling. Conformal calibration from CM's arXiv paper (2602.21368). TrustGate integration. Prediction set routing. One reliability number per system-task pair, with a mathematical guarantee.

30
lessons
6
modules
6–8
weeks self-paced
Get on the waitlist
€197 one-time · lifetime access
Lifetime access · Self-paced · Built on arXiv 2602.21368 + open-source TrustGate (Apache 2.0)
The gap

The model is confident. The model is wrong.
Those two things are unrelated.

Ask GPT-x a question. It answers with confidence. Ask it again. Same question. Same confidence. Sometimes a different answer.

The confidence is not a signal. It is a feature of the architecture. The model sounds certain because that is how language models produce text. It predicts the most probable next token. Certainty is the default tone, not the measured state.

You cannot ask the model if it's sure. It will say yes. It always says yes. You cannot ask it to rate its confidence. It will produce a number that has no mathematical relationship to its actual reliability.

You need an external system that measures reliability. Not asks about it. Measures it.

"You cannot deploy what you cannot measure. Reliability is not a feeling. It's a number."
The output · what the trust layer produces

One number per system-task pair. With a guarantee.

A reliability certificate you can show your security review, your finance team, your legal counsel. Not vibes. Not a benchmark. A measurement, with a calibrated coverage guarantee.

RELIABILITY CERTIFICATE
Systemdocument-qa-v2
Modelgpt-x.1
Taskenterprise-policy-qa
Reliability94.6%
Coverage95% (marginal)
Calibrated on50 examples
Routing
set 1→auto-approve(78%)
set 2–3→human-glance(17%)
set 4+→full-review(5%)
StatusDEPLOYABLE
The method

30 lessons. 6 modules.
From the confidence problem to a deployed trust layer.

Built on CM's published arXiv paper (2602.21368) and the open-source TrustGate implementation. The math is real. The code is production-ready.

Module 1 · Primer 00
"Why AI can't say 'I don't know.'"

The Confidence Problem

Why LLM calibration is structurally broken (helpfulness ≠ honesty). The four broken approaches: vibes, benchmarks, majority-vote, AI-as-judge. Bias-variance anatomy of LLM evaluation. From accuracy (a photograph) to reliability (a guarantee).

4 lessons
Module 2 01
"Repetition reveals reliability."

Self-Consistency Sampling

Theory and implementation: why asking N times reveals what asking once hides. Canonicalization (grouping before counting). Variance reduction via consensus aggregation. The stable-hallucination problem. Optimizing N: the stopping rule that saves 50% on API costs.

Lab: Self-consistency engine with configurable N and early stopping
5 lessons
Module 3 02
"From behavior to guarantee."

Conformal Calibration

The intuition: from N responses plus 50 human checks to one reliability number. Rank-based nonconformity scores. Prediction sets: set size 1 (auto-approve), 2-3 (human-glance), 4+ (full review). What the math promises and what it doesn't.

Lab: Implement conformal calibration from scratch (~100 lines), then TrustGate
5 lessons
Module 4 03
"The tool and the workflow."

TrustGate and Production

Architecture deep dive. Integration patterns: adding TrustGate to any LLM pipeline. Configuration: thresholds, prediction set sizes, routing rules. Sequential stopping. Behavioral drift detection via the golden-set pattern.

Lab: Integrate TrustGate into the E1 capstone
5 lessons
Module 5 04
"Evaluation-first, code to deployment."

The ADLC in Practice

The Accountable Development Lifecycle: Define KPIs → Build Eval Suite → Develop → Evaluate → Gate → Deploy → Monitor. Building golden test sets for your domain. Deployment gates. Continuous drift monitoring.

Lab: Complete ADLC pipeline with eval suite, deployment gate, drift detector
5 lessons
Module 6 · Capstone 05
"A guarantee, not a vibe."

The Trustworthy Document Q&A System

Take the E1 system and add the full trust pipeline. Self-consistency on every query. TrustGate with calibrated reliability levels. Prediction-set routing. Behavioral drift monitoring. Deployment gate that blocks unreliable releases. The result: a system with a number attached to its trustworthiness.

6 lessons + capstone
The capstone arc · across the series

One project. Six courses. Six layers.

In E2 your E1 capstone gets a trust layer with a mathematical guarantee. The same project carries forward through E3 to E6 into a deployed Enterprise AI Operating System.

E1 · Done
Foundation
Versioned prompts. Multi-model. MCP. GRAIL eval. Logging.
E2 · Now
+ Trust
Self-consistency. TrustGate. Reliability guarantees. Drift detection.
E3
+ Governance
7 services. Platform Protocol. ~15 APIs.
E4
+ Security
Guardrails. Agent Auth. Sandboxing. Red-team tested.
E5
+ Context
Multi-source. RAG. Context Router. RBAC.
E6
Full Platform
All 4 layers. Org Agents. Intelligence. Desktop Shell.
Prerequisites & tech stack

What you need. What you'll use.

Prerequisites

E1 completed (this course builds on the E1 capstone) or equivalent production AI engineering experience. Comfortable with Python. Basic statistics helpful but not required: the course teaches the math you need without requiring a stats background.

Tech stack

Python 3.12, FastAPI, Docker, TrustGate (Apache 2.0), NumPy and SciPy for conformal computation. The arXiv paper (2602.21368) annotated and walked through algorithm-by-algorithm.

Honestly

This is for you if:

→You build AI systems and need to prove reliability, not just claim it
→You need to satisfy compliance, legal, or audit requirements for AI outputs
→You want to reduce human review to only the cases that actually need it
→You've completed E1 or have equivalent production AI engineering experience
→You want the mathematical foundation for trust, not vibes-based confidence

Don't take this if:

→You don't write code. Start with the non-engineering courses.
→You haven't built a production AI service yet. Start with E1.
→You want to build agents. That's E3. Trust comes first.
Pricing

One price. Lifetime access.

€197
One-time payment. Lifetime access. All future updates included.
  • 30 lessons across 6 modules (video, written, runnable code)
  • Self-consistency engine and conformal calibration with TrustGate
  • Prediction-set routing system (auto-approve / human-glance / full-review)
  • Behavioral drift detector and complete ADLC pipeline
  • Annotated arXiv 2602.21368 walkthrough and full code repo (Apache 2.0)
3 months in the Engine Room. Where alumni and operators go to get unstuck.
Get on the waitlist
Lifetime access. All future updates included.

Want all six courses?

See the Engineering Series bundle →
FAQ

Before you ask.

The questions we hear most. If yours isn't here, email [email protected].

Do I need a research / math background?
No, but you need to be comfortable reading a formula. The course translates conformal prediction (CM's arXiv paper 2602.21368) into engineering code. The math is explained, the code is the lesson. Self-consistency sampling needs no math at all.
How is this different from the Verification Masterclass (€197)?
Verification Masterclass is the human discipline — how to read and gate AI output manually. Trust Engineering (E2) is the production layer — automatic reliability scoring with a mathematical guarantee. Both are €197. Pick Verification Masterclass if you're not shipping code; pick E2 if you are.
What does conformal prediction give me that GPT confidence scores don't?
A calibrated, formal guarantee. GPT's 'I'm 87% confident' is uncalibrated and unfalsifiable. Conformal prediction produces a number that holds across a test set you control, with a stated coverage rate. You can audit it. You can sign off on it.
Is TrustGate open source?
Yes. Apache 2.0. The repo is referenced in module 4 and ships with the course. You can adapt it to your stack.
Do I need E1 first?
Strongly recommended. The eval suite patterns and prompt architecture from E1 are assumed background. If you skip E1, plan extra hours to fill the gap or buy the Engineering Series bundle (€797) instead of separate courses.
Time commitment?
25–35 hours across 6 modules. Self-paced. Conformal calibration is the most demanding module — plan a full weekend for it.
Can my company pay for this?
Yes. Especially for ML / AI engineering teams. Invoices issued. Email [email protected] subject 'Reimbursement.'
What's the refund policy?
€197 courses are non-refundable. The Engineering Series bundle (€797) offers a 14-day conditional refund.

AI will never say "I don't know."

Your system must. Self-consistency. Conformal calibration. TrustGate. One reliability number per system-task pair. €197. Lifetime access.

Get on the waitlist
See the full series: The Engineering Series