Prove Your AI Works
Master AI Evaluation — the Skill Every Enterprise Is Desperate to Hire
Every AI team ships on vibes until the day it costs them. The engineers who can PROVE their AI works are the ones enterprises fight to hire.
All software is broken—AI systems most of all. The difference between teams that improve and teams that guess is evaluation. This masterclass turns the foundational research on judging AI systems into working infrastructure you build yourself. You'll trace the full progression: surface metrics like BLEU and ROUGE, semantic similarity with BERTScore and COMET, and judgment-based evaluation with LLM-as-a-judge. You'll detect and quantify hallucinations, then close the loop from evaluation to alignment with RLHF, constitutional AI, and red teaming. Evaluation becomes a design constraint, not an afterthought—and you become the engineer who can look at any AI system and say, with evidence, exactly where it fails and how to fix it.
Your Competitive Moat
AI Hyper-Personalizes Your Experience
This isn't a one-size-fits-all course. It's assessed to your gaps, adapted to you, and finished with a custom deliverable you build and own.
Pre-Masterclass Assessment
You begin with an AI-driven assessment that maps what you already know against everything this masterclass covers. We pinpoint your knowledge gaps up front—so your time goes only where it moves the needle.
An AI-Personalized Path
Your results reshape the masterclass around you. The AI aligns the material, examples, and pace to close your specific gaps—so a fixed curriculum becomes a path built for exactly one person: you.
A Custom Deliverable You Own
You don't leave with a certificate—you leave with a real, working artifact built for your goals. In "Prove Your AI Works," that means a deliverable you can ship, show, and build on. Something you made, not just something you watched.
Proven Transformation Results
Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats
📈 Career Transformation
💰 Business Impact
What You'll Actually Build
Choose Your Path to Mastery
All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.
Self-Paced Mastery
- All 8 modules available immediately
- Lifetime access to content and updates
- Community support and code reviews
- Monthly live office hours
8-Week Live Cohort
- Weekly live workshops with Dr. Lee
- Eval suite reviews on your real systems
- Direct instructor access
- Graduation certificate
- Alumni network access
Founder's Edition
- One-on-one mentorship with Dr. Lee
- Evaluation architecture for YOUR product
- Custom golden dataset design
- 90-day satisfaction guarantee
4-Day Immersive Bootcamp
Executive intensive format. Build your eval stack in one week. Live red-teaming labs.
Course Curriculum
8 transformative steps · 30 hours of hands-on content
Module 1: Evaluation as a Design Constraint
5 lessons · Shu-Ha-Ri cycle
- Why All AI Systems Are Broken—and Why That's Your Opportunity
- The Evaluation Taxonomy: Reference-Based, Reference-Free, Judgment-Based
- Failure Modes: How Teams Ship on Vibes and Pay Later
- Designing Systems Backward from Their Evaluation
- Your Evaluation Stack Roadmap
Module 2: Surface Metrics That Started It All
5 lessons · Shu-Ha-Ri cycle
- BLEU: N-Gram Precision and Its Blind Spots
- ROUGE: Recall-Oriented Evaluation for Summarization
- Exact Match, F1, and Token-Level Scoring
- When Surface Metrics Work—and When They Actively Mislead
- Hands-On: Build a Surface-Metric Harness from Scratch
Module 3: Semantic Similarity
5 lessons · Shu-Ha-Ri cycle
- From Strings to Meaning: Why Embeddings Changed Evaluation
- BERTScore: Token-Level Semantic Matching
- COMET: Learned Metrics That Track Human Judgment
- Calibrating Semantic Metrics for Your Domain
- Hands-On: Semantic Evaluation of a Real Generation Task
Module 4: LLM-as-a-Judge
5 lessons · Shu-Ha-Ri cycle
- Judgment-Based Evaluation: The Modern Standard
- Rubric Design: Turning Quality into Scoreable Criteria
- Judge Biases: Position, Verbosity, Self-Preference—and How to Control Them
- Calibrating Judges Against Human Labels
- Hands-On: Build a Calibrated LLM Judge Pipeline
Module 5: Hallucination Detection & Quantification
5 lessons · Shu-Ha-Ri cycle
- A Taxonomy of Hallucinations: Factual, Contextual, Logical
- Detection Strategies: Grounding Checks and Claim Verification
- Quantifying Hallucination Rates Reproducibly
- Tracking Hallucination Across Model and Prompt Changes
- Hands-On: Build a Hallucination Detector for a RAG System
Module 6: From Evaluation to Alignment
5 lessons · Shu-Ha-Ri cycle
- Closing the Loop: Evaluation Data as Training Signal
- RLHF in Practice: Preference Data and Reward Models
- Constitutional AI: Principles as Scalable Oversight
- Choosing Your Alignment Strategy by Failure Mode
- Hands-On: Turn Eval Failures into an Alignment Dataset
Module 7: Red Teaming
5 lessons · Shu-Ha-Ri cycle
- Adversarial Evaluation: Finding Failures Before Users Do
- Systematic Red Team Design: Coverage, Not Anecdotes
- Automated Adversarial Probing
- From Red Team Findings to Regression Suites
- Hands-On: Red Team a Production Assistant
Module 8: Evaluation Infrastructure That Lasts
5 lessons · Shu-Ha-Ri cycle
- Eval Suites as Code: Versioned, Repeatable, CI-Integrated
- Building Golden Datasets That Stay Golden
- Metrics Dashboards and Regression Alerts
- Timeless Practices That Survive Every Model Release
- Capstone: Ship a Complete Evaluation Stack for a Real System
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
Because every company shipped AI features in the last two years and almost none can prove they work. Evaluation engineering is now one of the most requested skills in enterprise AI hiring—the person who owns the eval stack owns the roadmap.
They're two halves of a reliability track. This masterclass is measurement: metrics, judges, hallucination detection, red teaming. AI Alignment and RLHF goes deep on the training side. Take this one first if you want the loop: measure, then align.
No—it's equally valuable if you build on APIs. Evaluation is how you compare vendors, catch regressions when models silently update, and prove your prompts and pipelines actually improved. It's the skill that survives every model release.
A complete evaluation stack: surface and semantic metric harnesses, a calibrated LLM-as-a-judge pipeline, a hallucination detector, a red-team regression suite, and CI-integrated eval infrastructure with dashboards.
Stop Renting AI. Start Owning It.
Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.
Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.
Self-paced · Lifetime access · 30-day guarantee
Start Your TransformationThis is not just education. This is technological sovereignty.