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AI Alignment and RLHF

The AI Alignment Mastery System™ — Build Models That Do What You Want

Master RLHF, DPO, and constitutional AI to build aligned models that reliably do what they're supposed to—the expertise separating $180K API users from $350K alignment architects.

Powerful AI models are useless if they're unpredictable, unsafe, or uncontrollable. This masterclass teaches you to build alignment systems from scratch—capable of reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), constitutional AI, and production safety deployment. You won't rely on HuggingFace Trainer APIs or black-box RLHF services—you'll build the foundations yourself: preference data collection, reward models, PPO/GRPO policy optimization, DPO direct alignment, and reasoning with RLVR.

This is not another course on calling OpenAI's fine-tuning API. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and alignment architects. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from API consumer to alignment specialist in 9 transformative modules.

Each module begins with a TedTalk-style presentation on alignment strategy, then you immediately build it yourself with hands-on coding. You'll implement complete RLHF pipelines, train reward models from preferences, optimize policies with PPO and DPO, create constitutional AI systems, deploy reasoning models using RLVR, and build production safety systems—not just call APIs.

Different from our LLM course: While "Build Your Own LLM" teaches you to construct base transformer models, this course focuses on making those models safe, controllable, and aligned with human values—the post-training techniques that transform raw models into production-ready systems like ChatGPT and Claude.

Different from our Reasoning course: While "Build Your Own Reasoning Model" teaches models to think step-by-step, this course teaches how to align any model behavior through human feedback and constitutional principles—the fundamental safety layer that prevents toxic outputs, ensures controllability, and builds stakeholder trust.

By the end, you won't just understand how RLHF works—you'll own production-ready alignment systems that become your competitive moat: As an engineer, you'll command $300K-$500K salaries at Anthropic/OpenAI/DeepMind with irreplaceable expertise in reward modeling, policy optimization, and constitutional AI that 99% of ML engineers lack. As a founder, you'll deploy AI that stakeholders and regulators trust, avoiding catastrophic failures that destroy competitors, while saving $500K+/year in API costs by owning aligned models instead of renting from OpenAI. When regulations mandate AI safety, you'll already be compliant. When models misbehave at 2am, you'll know exactly how to fix alignment—not blindly hope API providers do.

FROM
API Consumer
$100K-$150K · Replaceable Skills
TO
Model Builder
$250K-$400K · Irreplaceable
9 weeks · 50 hours · Own your model weights forever
Start Your TransformationSee The Journey
Why It's a Masterclass, Not a Course

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.

01Before You Start

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.

02During

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.

03Your Outcome

A Custom Deliverable You Own

You don't leave with a certificate—you leave with a real, working artifact built for your goals. In "AI Alignment and RLHF," that means a deliverable you can ship, show, and build on. Something you made, not just something you watched.

The LLM Sovereignty Stack™

Your 9-Step Transformation Journey

Each step follows the Shu-Ha-Ri method: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.Watch as you progress from API consumer to model builder, building your competitive moat with every step.

Weeks 1-3

PHASE 1: Alignment Foundations

From Unpredictable AI to Controlled Behavior

FROM
“Models generate toxic content, ignore instructions, produce unpredictable outputs—treating alignment as optional”
TO
“Building complete RLHF pipelines from scratch—reward models, preference data, policy optimization—with production safety”
🛡️ RLHF Pipeline Expertise
Ability to build and deploy reinforcement learning from human feedback systems that 99% of ML engineers can't—transforming unpredictable models into controllable, safe AI
Weeks 4-6

PHASE 2: Advanced Alignment Techniques

From Basic Training to Constitutional AI

FROM
“Using black-box API fine-tuning with no control, reward hacking failures, expensive alignment iterations”
TO
“Implementing DPO, constitutional AI, and RLVR for reasoning—owning the complete alignment stack with cost-efficient techniques”
🛡️ Direct Alignment Mastery
Expertise in DPO (Direct Preference Optimization) and constitutional AI that enables rapid alignment iterations without expensive reward model training
Weeks 7-9

PHASE 3: Production Deployment & Safety

From Lab Experiments to Trusted Production AI

FROM
“Models fail in production, no evaluation framework, manual safety monitoring, reactive incident response”
TO
“Production-grade alignment with comprehensive evaluation suites, safety monitoring, and continuous verification systems”
🛡️ Production Alignment Systems
Enterprise-ready safety infrastructure that ensures AI behaves correctly in production—avoiding catastrophic failures that destroy competitor products

The Complete Transformation Matrix

Each step follows the Shu-Ha-Ri cycle: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.This is the guided progression that transforms API consumers into model builders.

1

Module 1: Alignment Foundations

FROM (Point A)
“Treating alignment as optional, believing supervision is enough, viewing safety as post-deployment concern”
TO (Point B)
“Understanding alignment as critical production requirement, knowing why supervised learning fails for safety, treating alignment as core architecture”
🛡️ Strategic alignment thinking—recognizing when models need alignment vs. fine-tuning, understanding the alignment problem space
2

Module 2: Preference Intelligence

FROM (Point A)
“Using simple labels (good/bad), treating all feedback equally, manually collecting preferences at small scale”
TO (Point B)
“Building reward models from pairwise comparisons, understanding Bradley-Terry loss, implementing process vs. outcome reward models”
🛡️ Reward modeling expertise—ability to train models that predict human preferences, the foundation of all RLHF
3

Module 3: Training Dynamics

FROM (Point A)
“Models collapse during training, overfitting to reward signal, unstable policy updates destroying model quality”
TO (Point B)
“Implementing KL regularization, using reference models for stability, applying rejection sampling and instruction tuning”
🛡️ Stable alignment training—preventing mode collapse and reward hacking that causes 90% of alignment projects to fail
4

Module 4: Policy Optimization

FROM (Point A)
“Black-box understanding of PPO, can't debug alignment failures, dependent on HuggingFace trainers”
TO (Point B)
“Implementing PPO and GRPO from scratch in PyTorch, understanding clipping and GAE, debugging gradient issues”
🛡️ Policy optimization mastery—building PPO/GRPO implementations from first principles, not relying on abstracted frameworks
5

Module 5: Direct Alignment

FROM (Point A)
“Expensive two-stage RLHF (reward model + policy training), long iteration cycles, high compute costs”
TO (Point B)
“Implementing DPO for single-stage alignment, understanding when to use DPO vs. PPO, hybrid approaches”
🛡️ DPO expertise—faster, cheaper alignment iterations that reduce training costs by 50-70% compared to traditional RLHF
6

Module 6: Constitutional AI

FROM (Point A)
“Manually reviewing every output, can't scale safety monitoring, dependent on human labelers for all feedback”
TO (Point B)
“Building constitutional AI systems with AI feedback, automated red-teaming, recursive self-improvement loops”
🛡️ Scalable safety systems—using AI to monitor AI, enabling alignment at scale without proportional human labor
7

Module 7: Reasoning & Inference Scaling

FROM (Point A)
“Models that pattern-match without reasoning, can't solve complex math/code problems, limited to training data”
TO (Point B)
“Training reasoning models using RLVR (verifiable rewards), implementing process reward models, deploying test-time compute”
🛡️ Reasoning alignment expertise—building models that think before answering like DeepSeek R1 and o1, not just recall patterns
8

Module 8: Production Deployment

FROM (Point A)
“Lab-only alignment with no production validation, reactive failure response, manual quality monitoring”
TO (Point B)
“Building comprehensive evaluation suites, detecting over-optimization, implementing continuous alignment monitoring”
🛡️ Production safety infrastructure—systems that catch alignment failures before they reach users, not after disasters
9

Module 9: Advanced Techniques

FROM (Point A)
“One-time alignment with static behavior, manual data collection for every iteration, no feedback loops”
TO (Point B)
“Automated synthetic data generation, continuous alignment from user feedback, self-improving safety systems”
🛡️ Autonomous alignment systems—infrastructure that continuously improves model alignment from production feedback, compounding safety

The Shu-Ha-Ri Learning Method

Ancient Japanese martial arts philosophy adapted for elite technical education. Each module follows this complete cycle—by Step 9, you've experienced Shu-Ha-Ri nine times, building deeper mastery with every iteration.

📚

Shu (守) - Learn

TedTalk-style masterclass + guided hands-on coding

“Watch attention mechanisms explained, then code them yourself with step-by-step guidance”

🔨

Ha (破) - Break

Modify code, experiment with parameters, adapt to your problems

“Change attention heads from 8 to 12, try different learning rates, debug training instability”

🚀

Ri (離) - Transcend

Apply independently, innovate beyond what's taught

“Design novel architectures for your domain, solve your specific business problems, lead AI initiatives”

This is how you transcend from passive learner to active innovator. This is executive business education merged with hands-on mastery.

Proven Transformation Results

Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats

📈 Career Transformation

75%
Promoted to Senior+ within 12 months
$80K-$150K
Average salary increase
90%
Report being 'irreplaceable' at their company
85%
Lead AI initiatives after completion

💰 Business Impact

$150K/year
Average API cost savings from owning model weights
70%
Eliminate third-party model dependencies entirely
60%
Raise funding citing proprietary technology as moat
3-6 months
Average time to ROI on course investment

What You'll Actually Build

🏗️
Complete GPT
4,000+ lines of PyTorch
🧠
Attention
From scratch, no libraries
📊
Training
100M+ tokens
🎯
Classification
95%+ accuracy
💬
ChatBot
Instruction-following

Choose Your Path to Mastery

All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.

Self-Paced Mastery

$1,497
Lifetime Access
Self-directed learners
  • All 9 modules (45 hours of video content)
  • 100+ video lessons (TED-talk style instruction)
  • 50+ Jupyter notebooks with full implementations
  • Complete code templates (PPO, DPO, reward models, constitutional AI)
  • Quizzes and self-assessments
  • Private Discord community access
  • Course materials updated for life
  • Certificate of completion
  • Build and deploy complete RLHF pipeline (capstone project)
Most Popular

9-Week Live Cohort (Structured Transformation)

$5,997
12 Weeks
Engineers wanting accountability
  • Everything in Self-Paced PLUS:
  • 18 live sessions (2× weekly for 9 weeks)
  • Live Q&A with Dr. Lee during each session
  • Code reviews and personalized feedback
  • Weekly implementation workshops
  • Study groups and peer collaboration
  • Private mentorship Slack channel
  • Career support (resume, interviews, placement)
  • Capstone project with instructor review
  • Lifetime access to alumni network
  • Certificate of completion (verified)
  • Early bird pricing: $4,797 (save $1,200)

Founder's Edition (1:1 Implementation + Fractional CTO)

$17,997
6 Months
Founders & technical leaders
  • Everything in Cohort/Bootcamp PLUS:
  • 6 private 1-on-1 sessions with Dr. Lee (90 min each)
  • Custom alignment strategy for your specific use case
  • Complete code review of your production pipeline
  • Architecture design session for your AI product
  • Direct Slack/email access to Dr. Lee
  • Monthly lifetime office hours (30-min check-ins)
  • Introductions to hiring managers at Anthropic/OpenAI/DeepMind
  • VC introduction for AI safety-focused startups
  • Fractional CTO guidance on AI strategy
  • Custom deliverables: Alignment roadmap, tech audit, deployment plan
  • Optional: $4,997/month retainer for ongoing support

5-Day Intensive Bootcamp (Rapid Immersion)

All 9 modules compressed into intensive 5-day format. Daily live sessions (8am-5pm, 40 hours total). Hands-on labs each afternoon.

Course Curriculum

9 transformative steps · 45 hours of hands-on content

1

Module 1: Alignment Foundations

8 lessons · Shu-Ha-Ri cycle

  • Introduction: Why Alignment is Critical for Production AI
  • The Alignment Problem: Helpful, Honest, Harmless (HHH)
  • Historical Evolution: 2018 Origins to ChatGPT Era to DeepSeek R1
  • Core Definitions: Preferences, Rewards, Policies, Values
  • Language Modeling Overview and Post-Training Pipeline
  • The Problem Formulation: RLHF vs. Supervised Learning
  • Canonical Training Recipes: InstructGPT, Tülu 3, DeepSeek R1
  • Setting Up Your Complete RLHF Development Environment
2

Module 2: Preference Intelligence

9 lessons · Shu-Ha-Ri cycle

  • The Nature of Preferences: Why Preferences > Labels
  • Bias in Preference Data and Mitigation Strategies
  • Collecting Preference Data: Interface Design and Annotation
  • Rankings vs. Ratings: K-wise Comparisons
  • Structured Preference Data for Multi-Turn Conversations
  • Training Reward Models with Bradley-Terry Loss
  • Reward Model Architectures and Variants
  • Outcome Reward Models vs. Process Reward Models vs. Value Functions
  • Generative Reward Modeling for Explanations
3

Module 3: Training Dynamics

8 lessons · Shu-Ha-Ri cycle

  • KL Divergence Penalties: Preventing Model Collapse
  • Reference Models and Stable Training
  • Implementing KL Regularization in Policy Optimization
  • Pretraining Gradients and Regularization Techniques
  • Chat Templates and the Structure of Instructions
  • Best Practices of Instruction Tuning
  • Rejection Sampling: Data-Efficient Improvement
  • Best-of-N Sampling for Inference-Time Quality
4

Module 4: Policy Optimization

9 lessons · Shu-Ha-Ri cycle

  • Policy Gradient Algorithms: REINFORCE Foundation
  • Vanilla Policy Gradient Implementation
  • Proximal Policy Optimization (PPO) Mechanics
  • Implementing PPO from Scratch with Clipping
  • Group Relative Policy Optimization (GRPO)
  • Generalized Advantage Estimation (GAE) for Credit Assignment
  • Comparing PPO vs. GRPO for Language Models
  • Loss Aggregation, Asynchronicity, and Distributed Training
  • Debugging Gradient Explosion and Training Instabilities
5

Module 5: Direct Alignment

8 lessons · Shu-Ha-Ri cycle

  • Direct Preference Optimization (DPO): Eliminating Reward Models
  • How DPO Works: Mathematical Derivation
  • Implementing DPO Loss Function from Scratch
  • Numerical Concerns and DPO Weaknesses
  • DPO Alternatives: IPO, KTO, and Other Direct Methods
  • Online RL (PPO) vs. Offline (DPO): When to Use Each
  • Hybrid Approaches Combining DPO and PPO
  • Production DPO Pipelines at Scale
6

Module 6: Constitutional AI

8 lessons · Shu-Ha-Ri cycle

  • Constitutional AI Principles and Methodology
  • AI Feedback for Scalable Alignment
  • Using LLMs as Judges for Preference Generation
  • Designing Constitutional Principles for Your Use Case
  • Implementing Multi-Turn Constitutional AI Training
  • Automated Red-Teaming with AI Feedback
  • Recursive Self-Improvement Loops
  • Debugging AI Feedback Biases and Failure Modes
7

Module 7: Reasoning and Inference Scaling

9 lessons · Shu-Ha-Ri cycle

  • Why Reasoning Requires RL, Not Just Supervised Learning
  • The Origins of DeepSeek R1 and OpenAI o1
  • Verifiable Rewards vs. Preference-Based Rewards
  • Implementing Verifiable Reward Functions for Math/Coding
  • Training Reasoning Models with Outcome-Based RL
  • Building Process Reward Models for Step-by-Step Verification
  • Chain-of-Thought Reasoning and Inference-Time Scaling
  • Test-Time Compute: Improving Outputs with Search
  • Debugging Reward Hacking in Reasoning Tasks
8

Module 8: Production Deployment

9 lessons · Shu-Ha-Ri cycle

  • Function Calling and Tool Use for Agentic Behavior
  • Model Context Protocol (MCP) Implementation
  • Multi-Step Reasoning with Tool Orchestration
  • Modern Evaluation Beyond Static Benchmarks
  • Contamination Risks and Proper Holdout Sets
  • Detecting Over-Optimization and Proxy Reward Issues
  • Qualitative Over-Optimization: Sycophancy and Refusal
  • Building Comprehensive Evaluation Suites
  • Production Alignment Monitoring and Continuous Verification
9

Module 9: Advanced Techniques

10 lessons · Shu-Ha-Ri cycle

  • Synthetic Data Generation for Alignment at Scale
  • Distilling Alignment from Large Models to Smaller Ones
  • Style and Information Control: Chattiness vs. Conciseness
  • Model Character and Personality Training
  • Product UX and Alignment Integration
  • Model Specifications and Character Design
  • Building Autonomous Data Generation Pipelines
  • Product-Driven Alignment Iterations
  • Creating Feedback Loops: Users → Alignment → Deployment
  • Capstone: Deploy Your Complete Aligned AI System

Production-Grade Tech Stack

Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems

PythonPyTorchHuggingFace TransformersWeights & BiasesOpenAI GymAnthropic Constitutional AI

Frequently Asked Questions

Do I need RL background for this course?

No RL prerequisite required. We teach reinforcement learning from scratch specifically for alignment. If you understand supervised learning and backpropagation, you're ready. All RL concepts are built step-by-step.

How is this different from the LLM masterclass?

The LLM course teaches transformer architecture, pretraining, and tokenization—building the model. This course teaches post-training alignment: RLHF, DPO, constitutional AI—making models safe and controllable. They're complementary: LLM builds the engine, Alignment adds steering and safety.

Will I just use HuggingFace's RLHF trainer?

No. You'll implement PPO, DPO, and reward models from scratch in PyTorch to understand every component. Then you'll learn to use frameworks effectively, not blindly. Understanding comes from building.

Can I align models without massive compute?

Yes. We teach techniques that work on consumer GPUs and cloud instances. Reward models train on single GPUs. PPO/DPO fine-tuning works with small models (1B-7B parameters). You don't need GPT-4 scale compute to learn alignment.

Is this relevant for my industry (healthcare/finance/legal)?

Absolutely. Regulated industries need alignment more than anyone. We cover constitutional AI for compliance, preference data for domain expertise, and safety monitoring for production deployment. The techniques transfer to any domain.

Will this help me get hired at Anthropic/OpenAI/DeepMind?

This course teaches the exact techniques used at frontier labs. You'll build portfolio projects (RLHF pipelines, DPO implementations, constitutional AI systems) that demonstrate alignment expertise. Combined with our career support, alumni have landed roles at top AI companies.

How long until I can deploy aligned models in production?

By end of Week 5 (cohort) or Day 3 (bootcamp), you'll have a working DPO pipeline. By end of course, you'll deploy complete alignment systems. Self-paced students typically achieve production deployment within 60-90 days.

What's the ROI for my career?

Alignment specialists command $300K-$500K vs. $180K-$220K for ML generalists. The salary premium alone pays back the course investment 20x-100x in Year 1. Plus you become irreplaceable as alignment becomes critical for all AI deployment.

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.

Starting at
$1,497

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

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