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Build Your Own LLM

The LLM Sovereignty Stack™ — Stop Renting AI, Start Owning It

The ONLY masterclass teaching you to build production-ready LLMs from scratch—own your technology, stop renting from OpenAI.

This is not another course on using APIs. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and AI leaders. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from API consumer to model builder in 9 transformative steps. Each module begins with a TedTalk-style presentation, then you immediately build it yourself with hands-on coding. You'll construct a complete GPT architecture from scratch, train on real data, fine-tune for your use cases, and deploy with zero API dependency. By the end, you won't just understand how LLMs work—you'll own production-ready models that become your competitive moat. Available in 4 modalities: 9-Week Live Cohort, 5-Day Immersive Bootcamp, Self-Paced Mastery, or Founder's Edition (1:1 mentorship/Fractional CTO).

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
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

Foundation

Shu (守) - Learn the Fundamentals

FROM
“What is an LLM? How does attention work? I don't understand transformers.”
TO
“I can explain transformer architecture, code attention mechanisms from scratch, and prepare production data pipelines.”
🛡️ Knowledge Foundation
Speak the language of frontier AI—explain LLM architecture to executives, engineers, and investors with clarity and confidence.
Weeks 4-6

Implementation

Ha (破) - Build and Deploy

FROM
“I've never built a complete model from scratch. How do you train on real data?”
TO
“I've coded a complete GPT architecture, trained it on 100M+ tokens, and fine-tuned for classification tasks.”
🛡️ Implementation Mastery
Build and train models without libraries or APIs—own the complete pipeline from tokenization to deployment.
Weeks 7-9

Mastery

Ri (離) - Optimize and Lead

FROM
“Fine-tuning is expensive. Training is unstable. I can't deploy efficiently.”
TO
“I implement production-grade training with warmup/cosine decay/gradient clipping, use LoRA for efficient fine-tuning, and deploy instruction-following models.”
🛡️ Production Excellence
Match the training techniques of OpenAI and Google—optimize for speed, stability, and efficiency at scale.

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

Step 1: The Architecture of Intelligence

FROM (Point A)
“What is an LLM and how does it actually work?”
TO (Point B)
“I can explain transformer architecture, training paradigms, and the GPT design to technical leaders, investors, and engineering teams”
🛡️ Knowledge Moat
Speak the language of frontier AI
2

Step 2: Text as Data

FROM (Point A)
“How do models understand and process text?”
TO (Point B)
“I can tokenize text, create embeddings, encode positions, and prepare production-grade data pipelines for transformer training”
🛡️ Data Engineering Moat
Control the entire pipeline from raw text to training-ready batches
3

Step 3: The Attention Revolution

FROM (Point A)
“Attention mechanisms are too complex to understand”
TO (Point B)
“I can code self-attention, causal attention, and multi-head attention from scratch in PyTorch—the core innovation that powers all modern AI”
🛡️ Architectural Mastery Moat
Attention is the core innovation—master this and you can modify, optimize, and create novel architectures
4

Step 4: Architecting Language Models

FROM (Point A)
“I can't build a complete language model from scratch”
TO (Point B)
“I have coded a full GPT architecture that generates coherent text—every component, every line understood”
🛡️ Implementation Mastery Moat
You've built GPT from scratch—no more black boxes
5

Step 5: Training at Scale

FROM (Point A)
“How do you train an LLM on massive datasets?”
TO (Point B)
“I can pretrain language models from scratch using next-token prediction on large corpora—the $100M training process, demystified”
🛡️ Training Mastery Moat
Pretraining is the most expensive and valuable step—master this to train domain-specific models worth millions
6

Step 6: Task Specialization

FROM (Point A)
“How do you adapt LLMs for specific business tasks?”
TO (Point B)
“I can fine-tune pretrained models for classification tasks with custom heads and supervised learning—turning general models into specialized assets”
🛡️ Specialization Moat
General models are commodities—task-specific fine-tuned models with proprietary data are defensible assets
7

Step 7: Instruction Intelligence

FROM (Point A)
“How do you make models follow instructions like ChatGPT?”
TO (Point B)
“I can fine-tune models on instruction datasets to follow user commands and respond helpfully—the secret behind conversational AI”
🛡️ Instruction-Following Moat
This is what makes ChatGPT valuable—master this to create custom AI assistants for any domain
8

Step 8: Production Training Excellence

FROM (Point A)
“My training is slow and unstable”
TO (Point B)
“I implement production-grade training with warmup, cosine decay, and gradient clipping—techniques used by OpenAI and Google”
🛡️ Training Excellence Moat
These techniques separate hobbyist training from production training—achieve results 10x faster with better stability
9

Step 9: Efficient Adaptation at Scale

FROM (Point A)
“Fine-tuning is too expensive with billions of parameters”
TO (Point B)
“I use LoRA to fine-tune models with 0.1% of parameters and 10x faster training—the modern standard for efficient adaptation”
🛡️ Efficiency Moat
LoRA is how modern AI companies deploy hundreds of fine-tuned models—iterate 10x faster at 1/10th the cost

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

$1,997
Lifetime Access
Self-directed learners
  • All 9 modules available immediately
  • Lifetime access to content and updates
  • Community support and code reviews
  • Monthly live office hours
  • Learn on your own schedule
Most Popular

9-Week Live Cohort

$6,997
12 Weeks
Engineers wanting accountability
  • Weekly live workshops with Dr. Lee
  • Cohort accountability and peer learning
  • Direct instructor access
  • Graduation certificate
  • Alumni network access
  • Fixed start dates (4 cohorts per year)

Founder's Edition

$19,997
6 Months
Founders & technical leaders
  • One-on-one mentorship with Dr. Lee
  • Custom learning path for your specific needs
  • Build YOUR proprietary model with guidance
  • Fractional CTO services (for funded startups)
  • Architecture consulting and strategic advising
  • 90-day satisfaction guarantee

5-Day Immersive Bootcamp

Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.

Course Curriculum

9 transformative steps · 50 hours of hands-on content

1

Step 1: The Architecture of Intelligence

7 lessons · Shu-Ha-Ri cycle

  • The Nature of Language Models: From Pattern Matching to Understanding
  • Real-World Applications and Possibilities: Where LLMs Create Business Value
  • The Three-Stage Journey: Build, Train, Deploy
  • Why Transformers Changed Everything: The Attention Revolution
  • Data: The Foundation of Intelligence
  • Deconstructing the GPT Blueprint: Every Component Explained
  • Your Roadmap to Model Ownership: What You'll Build
2

Step 2: Text as Data

8 lessons · Shu-Ha-Ri cycle

  • Semantic Space: How Words Become Vectors
  • Breaking Text into Intelligent Chunks: Tokenization Mastery
  • Building the Model's Vocabulary: Token-to-ID Mapping
  • Strategic Special Tokens for Context Control
  • Byte Pair Encoding: The Production Standard (GPT-3/4, Claude, Llama)
  • Efficient Data Sampling Strategies: Sliding Windows
  • Learning Semantic Representations: Embedding Layers
  • Position Encoding: Teaching Order to Parallel Systems
3

Step 3: The Attention Revolution

11 lessons · Shu-Ha-Ri cycle

  • Why Sequential Models Hit a Wall: The Case for Attention
  • The Attention Mechanism: Weighted Relevance Explained
  • Self-Attention: The Simplest Form (10 Lines of Python)
  • Scaling Attention to Full Sequences: Batched Implementation
  • Queries, Keys, Values: The Trainable Triplet
  • Building Reusable Attention Components
  • Causal Masking: The Secret of Text Generation
  • Dropout: Preventing Attention Overfitting
  • Building Production Causal Attention
  • Why Multi-Head Attention Outperforms Single-Head
  • Efficient Multi-Head Implementation: Parallel Computation
4

Step 4: Architecting Language Models

7 lessons · Shu-Ha-Ri cycle

  • Assembling the Complete Architecture: Embeddings → Transformer → Head
  • Layer Normalization for Training Stability
  • Feed-Forward Networks: The Other Half of Transformers
  • Residual Connections: Enabling Deep Learning
  • Building the Transformer Block: Modular Design
  • Implementing the Full GPT Model: 4,000+ Lines You Understand
  • Text Generation: Bringing Models to Life with Temperature Sampling
5

Step 5: Training at Scale

9 lessons · Shu-Ha-Ri cycle

  • Why Untrained Models Generate Noise: The Need for Pretraining
  • The Loss Function: Measuring Learning (Cross-Entropy)
  • Training vs Validation: Preventing Overfitting
  • The Complete Training Loop: Forward, Loss, Backprop, Optimizer
  • Temperature: Controlling Creativity (High = Creative, Low = Deterministic)
  • Top-K Sampling: Quality Control for Generation
  • Flexible Generation Functions: Customizable Decoding
  • Persisting Model Weights: Deployment Readiness
  • Leveraging Pretrained Weights: Loading GPT-2 for Transfer Learning
6

Step 6: Task Specialization

8 lessons · Shu-Ha-Ri cycle

  • The Fine-Tuning Landscape: Classification vs Instruction vs RLHF
  • Data Preparation for Classification: Labeled Datasets
  • Efficient Data Loading: PyTorch DataLoaders
  • Transfer Learning Strategy: Freeze/Unfreeze Layers
  • Adding Task-Specific Heads: Linear Projection Layers
  • Training with Supervised Signals: Cross-Entropy on Class Distributions
  • Fine-Tuning in Practice: 3-5 Epochs to Production
  • Real-World Deployment: 95%+ Accuracy on Spam Detection
7

Step 7: Instruction Intelligence

9 lessons · Shu-Ha-Ri cycle

  • The Foundation of Helpful AI: How ChatGPT Was Created
  • Formatting Instruction Data: (Instruction, Input, Output) Triples
  • Batching Conversational Data: Padding and Attention Masks
  • Building Instruction Data Loaders: Custom Collate Functions
  • Choosing Your Starting Point: Pretrained vs From Scratch
  • Training Instruction-Following Behavior: Supervised Fine-Tuning
  • Capturing Model Responses: Generation and Evaluation
  • Evaluating AI Assistant Quality: Helpfulness, Accuracy, Safety
  • The Path to Alignment: RLHF and Beyond
8

Step 8: Production Training Excellence

6 lessons · Shu-Ha-Ri cycle

  • Warm Start: Preventing Early Instability with Learning Rate Warmup
  • Cosine Annealing: Smooth Convergence with LR Scheduling
  • Gradient Clipping: Explosive Gradient Protection
  • The Production Training Function: Warmup + Cosine + Clipping + Logging
  • GPU Optimization: Making Training 10x Faster
  • Monitoring Training: TensorBoard and Weights & Biases
9

Step 9: Efficient Adaptation at Scale

7 lessons · Shu-Ha-Ri cycle

  • Low-Rank Adaptation Explained: How Modern ChatGPT/Gemini/Claude Fine-Tune
  • Preparing Data for Efficient Training: Same Data, 10x Faster
  • Injecting LoRA Adapters: Freezing Weights, Training Low-Rank Matrices
  • Training with LoRA: 0.1% Parameters, 95-100% Performance
  • Comparing LoRA vs Full Fine-Tuning: Cost-Benefit Analysis
  • Multi-Task Adaptation: Swapping LoRA Adapters for Different Tasks
  • Deployment Strategies: Serving Multiple Fine-Tuned Models Efficiently

Production-Grade Tech Stack

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

For Career Advancers

I help AI engineers and technical leaders build production-ready large language models from scratch, so they can command $250K-$400K salaries and become irreplaceable AI architects without depending on OpenAI's API, paying $5K-$50K/month in usage fees, or being viewed as just another 'prompt engineer' who doesn't understand how models actually work.

For Founders & CTOs

I help technical founders and CTOs build proprietary large language models that create defensible competitive moats, so they can save $200K-$500K in API costs annually and own their model weights without being held hostage by OpenAI rate limits, vendor lock-in, or spending $300K-$500K hiring ML engineers who may not deliver.

PyTorchTiktokenGPT-2LoRAWeights & BiasesCUDAHugging Face

Frequently Asked Questions

Who is this masterclass for?

This is for AI engineers earning $100K-$150K who want to command $250K-$400K salaries, and for technical founders burning $5K-$50K/month on APIs who want to own their technology. If you're tired of being an API consumer and want to become a model builder, this is for you.

What's included in the different modalities?

Self-Paced ($1,997): All 9 modules, lifetime access, community support. 9-Week Cohort ($6,997): Live workshops, direct instructor access, accountability. 5-Day Bootcamp: Intensive executive format. Founder's Edition ($19,997): 1:1 coaching, custom architecture consulting, or Fractional CTO services.

What technical background do I need?

Intermediate Python skills and basic ML concepts. This is hands-on implementation using the Shu-Ha-Ri method: TedTalk-style inspiration + guided coding + experimentation. No PhD required. If you can code in Python, you're ready.

What hardware do I need?

Any modern laptop. GPU acceleration is optional—we provide cloud GPU options for faster training. The models you build will run locally on your machine. No specialized hardware required.

Will I actually build a working GPT model?

Yes. You'll build a complete GPT architecture from scratch (4,000+ lines of PyTorch), train on 100M+ tokens, fine-tune for classification and instruction-following, and deploy with zero API dependency. This is not a toy project—it's production-ready code.

How is this different from using the OpenAI API?

APIs are rented capability—you own nothing. This masterclass teaches you to OWN model weights. Stop paying $50K/year to OpenAI. Build proprietary models that become your competitive moat. Understand every line of code, customize architectures, eliminate API costs forever.

What's the ROI for engineers vs founders?

Engineers: Avg $80K-$150K salary increase within 12 months. 75% promoted to Senior+. Command $250K-$400K as irreplaceable AI architect. Founders: Save $100K-$500K/year in API costs. Build defensible moat. Raise Series A on proprietary technology. ROI in 3-6 months.

What's the DrLee.AI Shu-Ha-Ri learning method?

Shu (Learn): TedTalk-style masterclass + hands-on coding. Ha (Break): Modify architectures, experiment, adapt to your problems. Ri (Transcend): Innovate beyond what's taught, lead AI initiatives. Each module follows: Inspire → Implement → Integrate → Innovate.

Can I load pretrained weights like GPT-2?

Yes. You'll learn to load pretrained weights (GPT-2, Llama, etc.) into your custom architecture, giving you a powerful starting point for fine-tuning without training from scratch. Best of both worlds: understand the internals + leverage existing pretraining.

What if I'm not satisfied?

30-day money-back guarantee for Self-Paced and Cohort tiers. No questions asked. For Founder's Edition: 90-day satisfaction guarantee—we'll work with you until you achieve results or refund 50% (reflecting value already delivered).

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
$1,997

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

30-day guarantee
Lifetime updates
Zero API costs forever