Teach Your AI What Humans Want
The RLHF Post-Training Stack™ — Build the Alignment Layer Behind Every Frontier Model
Pretraining gives a model knowledge. RLHF gives it judgment. Master the post-training layer that turned raw language models into assistants people actually trust.
Reinforcement Learning from Human Feedback is how a capable-but-unruly base model becomes a helpful, harmless, honest assistant. It's the technique that made ChatGPT feel different—and the post-training layer every frontier lab now depends on. This masterclass takes you inside RLHF from first principles: why it emerged to solve hard-to-specify problems, how to collect and model human preferences, how to train reward models, and how to optimize policies with the full post-training toolkit. You'll work through preference data, reward modeling, policy optimization, synthetic data generation, and modern alternatives like DPO—then wire it all into a working post-training pipeline. This is the deep, build-it-yourself companion to alignment: not the philosophy of alignment, but the machinery that produces 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 "Teach Your AI What Humans Want," 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 9 modules available immediately
- Lifetime access to content and updates
- Community support and code reviews
- Monthly live office hours
9-Week Live Cohort
- Weekly live workshops with Dr. Lee
- Pipeline reviews on your models
- Direct instructor access
- Graduation certificate
- Alumni network access
Founder's Edition
- One-on-one mentorship with Dr. Lee
- Post-training strategy for YOUR model
- Custom reward-model design
- 90-day satisfaction guarantee
5-Day Immersive Bootcamp
Executive intensive format. Build an RLHF pipeline in one week. Live reward-modeling labs.
Course Curriculum
9 transformative steps · 40 hours of hands-on content
Module 1: Why RLHF Exists
5 lessons · Shu-Ha-Ri cycle
- The Hard-to-Specify Problem: When You Can't Write the Reward
- From Base Model to Assistant: The Post-Training Layer
- How RLHF Changed What AI Feels Like
- The RLHF Pipeline at a Glance
- Your Post-Training Roadmap
Module 2: Human Preferences as Data
5 lessons · Shu-Ha-Ri cycle
- What Counts as a Preference: Pairwise, Ranked, and Rated
- Designing Preference Collection That Isn't Noise
- Annotator Guidelines and Inter-Rater Reliability
- Preference Data Quality Gates
- Hands-On: Build a Preference Dataset
Module 3: Reward Modeling
5 lessons · Shu-Ha-Ri cycle
- Turning Preferences into a Scalar Reward
- Training a Reward Model from Pairwise Data
- Reward Model Evaluation and Calibration
- Reward Hacking: How Models Game the Signal
- Hands-On: Train Your First Reward Model
Module 4: Policy Optimization Foundations
5 lessons · Shu-Ha-Ri cycle
- RL for Language Models: The Core Loop
- PPO and the Post-Training Objective
- KL Penalties: Staying Close to the Base Model
- Stability Techniques for RL Fine-Tuning
- Hands-On: Optimize a Policy Against Your Reward Model
Module 5: Direct Preference Optimization
5 lessons · Shu-Ha-Ri cycle
- DPO: Skipping the Reward Model
- When DPO Beats Full RLHF—and When It Doesn't
- Implementing DPO End to End
- Comparing DPO and PPO Head to Head
- Hands-On: Align a Model with DPO
Module 6: Synthetic Data for Feedback
5 lessons · Shu-Ha-Ri cycle
- Scaling Beyond Human Labels with Synthetic Data
- AI Feedback: RLAIF and Constitutional Approaches
- Quality Control for Synthetic Preferences
- Blending Human and Synthetic Signal
- Hands-On: Generate and Validate Synthetic Preferences
Module 7: Instruction & Post-Training Recipes
5 lessons · Shu-Ha-Ri cycle
- Supervised Fine-Tuning as the Foundation
- Sequencing SFT, Reward Modeling, and RL
- Post-Training Recipes Used by Frontier Labs
- Ablations: Knowing Which Step Did What
- Hands-On: Run a Full SFT-to-RLHF Sequence
Module 8: Safety in the Loop
5 lessons · Shu-Ha-Ri cycle
- Safety Regressions at Every Post-Training Step
- Red Teaming the Aligned Model
- Measuring Helpfulness vs Harmlessness Tradeoffs
- Guardrails Against Alignment Regressions
- Hands-On: Add Safety Regressions to Your Pipeline
Module 9: The Complete Post-Training Pipeline
5 lessons · Shu-Ha-Ri cycle
- Assembling Preferences, Reward, Policy, and Safety
- Evaluating the Aligned Model End to End
- Deploying and Monitoring an RLHF'd Model
- Timeless Practices as Methods Evolve
- Capstone: Ship a Working RLHF Pipeline
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
AI Alignment and RLHF is the broad alignment course—DPO, constitutional AI, and the theory of building models that do what you want. This masterclass is a deep, hands-on build of the RLHF post-training pipeline specifically: preference data, reward modeling, policy optimization, synthetic feedback, and safety regressions. Think of it as the machine-room companion to the alignment course.
You should be comfortable with PyTorch and fine-tuning. If you've taken Build Your Own LLM or Fine-Tune Your Own Models, you're well prepared. RLHF sits on top of a base or instruction-tuned model, which you'll start from.
Yes—and you'll learn both. DPO simplifies parts of the pipeline but doesn't replace understanding reward modeling and policy optimization. You'll implement both and learn exactly when each wins.
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.