Master Context Engineering
Stop AI Hallucinations at the Source — the #1 Skill Behind Reliable AI
Your agents don't fail because the model is weak. They fail because you fed them the wrong context. Master the discipline behind every great AI system.
Prompt engineering was the warm-up. Context engineering is the real game: the discipline of selecting, organizing, compressing, and prioritizing exactly what enters the context window—and what stays out. With million-token contexts and autonomous agents, sloppy context means hallucinations, brittle agents, and unpredictable output. In this masterclass you'll engineer the complete context stack from first principles: instruction design and agent skills, retrieval grounding, MCP tool integration, memory and state, and multi-agent context coordination. Each module starts with a TedTalk-style deep dive, then you build the system yourself. By the end, you'll think like a context system designer—knowing exactly what gets retrieved just-in-time, what becomes persistent memory, and why your agents suddenly stopped hallucinating.
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 "Master Context Engineering," 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 7 modules available immediately
- Lifetime access to content and updates
- Community support and code reviews
- Monthly live office hours
- Learn on your own schedule
7-Week Live Cohort
- Weekly live workshops with Dr. Lee
- Cohort accountability and peer learning
- Direct instructor access
- Graduation certificate
- Alumni network access
Founder's Edition
- One-on-one mentorship with Dr. Lee
- Context architecture review for YOUR product
- Custom implementation guidance
- 90-day satisfaction guarantee
4-Day Immersive Bootcamp
Executive intensive format. Hands-on labs with immediate feedback. Build your context engine in one week.
Course Curriculum
7 transformative steps · 35 hours of hands-on content
Module 1: The Context Revolution
5 lessons · Shu-Ha-Ri cycle
- Why Context Beats Prompts: The Discipline Nobody Named Until Now
- Anatomy of a Context Window: What the Model Actually Sees
- The Cost of Bad Context: Hallucinations, Brittle Agents, Unpredictable Output
- From Prompts to Workflows to Agents: Why Automation Is Non-Negotiable
- The Context Engineering Stack: Your Roadmap for This Masterclass
Module 2: Instruction Architecture & Agent Skills
5 lessons · Shu-Ha-Ri cycle
- System Prompts as Code: Designing Instructions with Engineering Rigor
- Agent Skills: Packaging Expertise the Model Can Load On Demand
- Instruction Artifacts: Versioning, Testing, and Reusing Your Instructions
- Instruction Hierarchies: What Wins When Instructions Conflict
- Hands-On: Build a Skill-Driven Instruction Layer for Your Agent
Module 3: Grounding with Retrieval
5 lessons · Shu-Ha-Ri cycle
- RAG as Context Selection: Retrieval Is a Filter, Not a Firehose
- Chunking, Embedding, and Indexing for Precision Retrieval
- Content Filtering and Reranking: Only the Relevant Survives
- Advanced RAG Patterns: Query Transformation and Hybrid Search
- Hands-On: Build a Grounding Pipeline That Kills Hallucinations
Module 4: Tools Through MCP
5 lessons · Shu-Ha-Ri cycle
- Function Calling from First Principles: APIs, Services, and Code
- The Model Context Protocol: Standardized Tool Discovery
- The CLI as a Tool Surface: Direct System Interaction
- Tool Context Budgeting: Exposing Capability Without Bloat
- Hands-On: Wire Your Agent into the MCP Ecosystem
Module 5: Memory & State
5 lessons · Shu-Ha-Ri cycle
- Short-Term vs Long-Term Memory: What to Keep, What to Forget
- Context Growth Management: Sliding Windows, Compaction, Summarization
- Persistent State: Sessions, Profiles, and Knowledge That Survives Restarts
- Memory Retrieval: Bringing the Right Past into the Present
- Hands-On: Add a Memory Hierarchy to Your Context Engine
Module 6: Multi-Agent Context Coordination
5 lessons · Shu-Ha-Ri cycle
- Context Isolation: Why Agents Poison Each Other's Windows
- Shared Context Stores: Blackboards, Artifacts, and Handoffs
- Orchestration Patterns: Who Knows What, and When
- Context Routing: Delivering the Right Slice to the Right Agent
- Hands-On: Coordinate Context Across a Research Agent Team
Module 7: Production Context Systems
5 lessons · Shu-Ha-Ri cycle
- Context Observability: Tracing Every Token into the Window
- Evaluating Context Quality: Relevance, Freshness, and Efficiency Metrics
- Cost Engineering: Caching, Compression, and Token Budgets at Scale
- Failure Modes: Context Poisoning, Drift, and Silent Truncation
- Capstone: Ship a Production-Grade Context Engine End to End
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
Prompt engineering is writing better sentences. Context engineering is systems design: programmatically selecting, retrieving, compressing, and prioritizing everything that enters the model's window—instructions, retrieved knowledge, tool results, memory, and state. Prompts are one input; you'll engineer the whole pipeline.
No, but they pair perfectly. This masterclass is the connective tissue between Build Your Own Autonomous AI Agent and Build Your Own Multi-Agent AI Teams—the layer that determines whether those systems are reliable or brittle. Intermediate Python is the only hard requirement.
You'll build the core mechanisms from first principles, then see how tools like DSPy, LangChain, and LlamaIndex implement them—working against frontier models from Anthropic, OpenAI, and Google. You learn the discipline, not a framework, so your skills survive every new release.
A complete, production-grade context engine: instruction and skill layer, retrieval grounding, MCP tool integration, memory hierarchy, multi-agent context coordination, and observability. It's infrastructure you can drop under any agent you ever build.
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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.