From Prototype to Production
The Reliability Engineering That Turns AI Demos Into Products You Can Trust
The demo took a weekend. Production is where AI products die. Learn the reliability engineering that separates prototypes from products.
Every team has a promising AI prototype. Almost none can grow it into a product that survives real users, real data, and real compliance. This masterclass teaches AI reliability as an engineering discipline across six dimensions—accuracy and grounding, safe agency, graceful failure, consistency, fairness, and operational efficiency—organized into a three-layer framework: Reliable Outputs (prompts, RAG, model customization), Reliable Agents (memory, tool use, orchestration), and Reliable Operations (deployment, monitoring, responsible AI). You'll measure everything with fine-grained, LLM-native rubrics like Grounding Defect Rate, Hallucination Severity Score, and FActScore—auditing not just outputs but the entire step-by-step reasoning trajectory of autonomous agents. You'll build real projects, including a multi-agent travel planner and a medical assistant, using industry standards like LangGraph and MCP, and finish with reference architectures and decision checklists you'll reuse for every system you ever ship.
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 "From Prototype to Production," 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 11 modules available immediately
- Lifetime access to content and updates
- Community support and code reviews
- Monthly live office hours
10-Week Live Cohort
- Weekly live workshops with Dr. Lee
- Reliability audits of your real systems
- Direct instructor access
- Graduation certificate
- Alumni network access
Founder's Edition
- One-on-one mentorship with Dr. Lee
- Reliability audit of YOUR production system
- Compliance architecture guidance
- 90-day satisfaction guarantee
5-Day Immersive Bootcamp
Executive intensive format. Harden a real system in one week. Live incident-response simulations.
Course Curriculum
11 transformative steps · 40 hours of hands-on content
Module 1: The Six Dimensions of AI Reliability
5 lessons · Shu-Ha-Ri cycle
- Why 90% of AI Prototypes Never Become Products
- Defining Reliability: Accuracy, Safe Agency, Graceful Failure, Consistency, Fairness, Efficiency
- The Three-Layer Framework: Outputs, Agents, Operations
- You Cannot Improve What You Do Not Measure
- Auditing a Fragile Prototype: Your Baseline
Module 2: Reliable Outputs—Prompt Engineering for Consistency
5 lessons · Shu-Ha-Ri cycle
- Well-Engineered Prompts vs Lucky Prompts
- Structured Outputs and Schema Enforcement
- Determinism Controls: Temperature, Seeds, and Sampling
- Prompt Regression Testing
- Hands-On: Stabilize an Inconsistent Assistant
Module 3: Reliable Outputs—Grounding with RAG
5 lessons · Shu-Ha-Ri cycle
- Grounding Outputs in Real Business Data
- Retrieval Quality: The Root of Most Hallucinations
- Citation and Attribution Patterns
- Grounding Defect Rate: Measuring What Slipped Through
- Hands-On: Ground a Q&A System and Prove It
Module 4: Reliable Outputs—Model Customization
5 lessons · Shu-Ha-Ri cycle
- When Prompting Isn't Enough: The Customization Decision
- Fine-Tuning for Consistency and Domain Fit
- Model Compression and Quantization Without Quality Loss
- Version Pinning and Upgrade Discipline
- Hands-On: Customize a Model for a Reliability Target
Module 5: LLM-Native Evaluation Rubrics
5 lessons · Shu-Ha-Ri cycle
- Beyond Accuracy: Fine-Grained Quality Measurement
- Hallucination Severity Score: Not All Errors Are Equal
- FActScore: Auditing Factual Precision Claim by Claim
- Building Rubrics Your Whole Team Can Run
- Hands-On: Score a Real System Across All Rubrics
Module 6: Reliable Agents—Memory
5 lessons · Shu-Ha-Ri cycle
- Agent Memory as a Reliability Surface
- Memory Corruption, Staleness, and Contamination
- Bounding What Agents Remember and Retrieve
- Testing Memory Behavior Over Long Horizons
- Hands-On: Harden an Agent's Memory Layer
Module 7: Reliable Agents—Safe Tool Use
5 lessons · Shu-Ha-Ri cycle
- Tools Are Where Agents Touch the Real World
- Permission Boundaries and Blast-Radius Design
- Validating Tool Inputs and Outputs
- Human Approval Gates for Consequential Actions
- Hands-On: Add Safety Rails to a Tool-Using Agent
Module 8: Reliable Agents—Orchestration
5 lessons · Shu-Ha-Ri cycle
- Safe, Consistent Multi-Step Workflows with LangGraph
- MCP Integration Without Losing Control
- Trajectory Evaluation: Auditing Every Step, Not Just the Answer
- Failure Recovery in Multi-Agent Flows
- Hands-On: Build the Multi-Agent Travel Planner
Module 9: Reliable Operations—Deployment
5 lessons · Shu-Ha-Ri cycle
- Semantic Caching: Faster and Cheaper Without Staleness
- Multi-Model Fallbacks: Surviving Provider Outages
- Graceful Degradation Under Load
- Cost Engineering as a Reliability Practice
- Hands-On: Deploy with Caching and Fallbacks
Module 10: Reliable Operations—Monitoring & Responsible AI
5 lessons · Shu-Ha-Ri cycle
- Production Monitoring for AI-Specific Failures
- Drift Detection and Regression Alerts
- Fairness Auditing in Production
- Compliance Patterns: HIPAA, GDPR, and Enterprise Standards
- Hands-On: Build the Monitoring Layer
Module 11: Capstone—The Medical Assistant
5 lessons · Shu-Ha-Ri cycle
- Applying All Three Layers to a High-Stakes Domain
- Reference Architecture Walkthrough
- Decision Checklists for Every Future System
- Capstone: Ship a Compliant, Monitored, Trustworthy Assistant
- Your Reliability Playbook Going Forward
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
Prove Your AI Works teaches measurement as a discipline—metrics, judges, red teaming. From Prototype to Production is the systems course: it uses those measurements inside a full reliability framework covering prompts, RAG, agents, deployment, monitoring, and compliance. Together they form the complete reliability track.
Both. The three-layer framework applies whether you call Claude or serve your own weights. Multi-model fallbacks, semantic caching, and trajectory auditing matter in every architecture.
A multi-agent travel planner (orchestration and trajectory auditing) and a medical assistant (grounding, compliance, and monitoring in a high-stakes domain)—plus the reusable reference architecture and checklists you'll apply to your own systems.
Basic familiarity with LLM apps is enough. If you've taken Build Your Own Autonomous AI Agent, you'll move faster through the agent modules, but everything is built up from working code.
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