Build Your Own GraphRAG System
Knowledge Graphs That Outperform Vector Search—Build, Don't Rent
RAG only works when you find the right context. Knowledge graphs ensure you always do.
Retrieval Augmented Generation is powerful, but only when you can quickly identify and supply the most relevant context to your LLM. This masterclass shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. You'll learn vector similarity-based approaches, semantic layers, agentic RAG patterns, and how to generate Cypher queries that retrieve precisely the data your LLM needs. By the end, you'll have built a production-quality GraphRAG system that dramatically outperforms basic vector search.
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 "Build Your Own GraphRAG System," 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 steps available immediately
- 50 hours of content + code
- Lifetime access to updates
- Community support
- Monthly live office hours
- 30-day money-back guarantee
Live Cohort
- Everything in Self-Paced
- Weekly live workshops (2 hrs)
- Direct instructor access
- Cohort accountability & networking
- 24-hour code review turnaround
- 1-on-1 kickoff & graduation calls
- Certificate + alumni network
Founder's Edition
- Everything in Live Cohort
- 6 monthly 1-on-1 coaching calls
- Fractional CTO advisory and implementation support
- Custom learning path for your business
- Same-day code reviews
- Architecture consulting for your product
- Your proprietary model built with you
- Investor pitch coaching
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
8 transformative steps · 35 hours of hands-on content
Module 1: Improving LLM Accuracy
7 lessons · Shu-Ha-Ri cycle
- Executive Overview: Why RAG Matters for Production AI
- Introduction to LLMs and Their Limitations
- The Knowledge Cutoff Problem
- Hallucinations and Outdated Information
- Lack of Private Information
- Supervised Fine-tuning vs. Retrieval-Augmented Generation
- Knowledge Graphs as Data Storage for RAG Applications
Module 2: Vector Similarity Search and Hybrid Search
8 lessons · Shu-Ha-Ri cycle
- Components of a RAG Architecture: Retriever and Generator
- RAG Using Vector Similarity Search
- Text Chunking Strategies
- Embedding Models: Choosing and Using
- Database with Vector Similarity Search Function
- Performing Vector Search
- Generating Answers Using an LLM
- Adding Full-Text Search for Hybrid Retrieval
Module 3: Advanced Vector Retrieval Strategies
5 lessons · Shu-Ha-Ri cycle
- Step-Back Prompting: Asking Better Questions
- Parent Document Retriever Pattern
- Retrieving Parent Document Strategy Data
- Building the Complete RAG Pipeline
- Combining Strategies for Better Results
Module 4: Generating Cypher Queries from Natural Language
8 lessons · Shu-Ha-Ri cycle
- The Basics of Query Language Generation
- Where Query Language Generation Fits in the RAG Pipeline
- Using Few-Shot Examples for In-Context Learning
- Using Database Schema to Guide the LLM
- Terminology Mapping: Semantically Mapping Questions to Schema
- Implementing a Text2Cypher Generator
- Specialized Fine-Tuned LLMs for Text2Cypher
- What Text2Cypher Enables for Your Applications
Module 5: Agentic RAG
9 lessons · Shu-Ha-Ri cycle
- What is Agentic RAG?
- Retriever Agents: Specialized Retrieval Components
- The Retriever Router: Choosing the Right Tool
- Answer Critic: Validating Generated Responses
- Why We Need Agentic RAG
- Implementing Retriever Tools
- Implementing the Retriever Router
- Implementing the Answer Critic
- Tying It All Together: A Complete Agentic RAG System
Module 6: Constructing Knowledge Graphs with LLMs
7 lessons · Shu-Ha-Ri cycle
- Extracting Structured Data from Text
- Structured Outputs Model Definition
- Structured Outputs Extraction Request
- Working with Real Datasets (CUAD Example)
- Constructing the Graph: Data Import
- Entity Resolution: Deduplicating Entities
- Adding Unstructured Data to the Graph
Module 7: Microsoft's GraphRAG Implementation
8 lessons · Shu-Ha-Ri cycle
- The Microsoft GraphRAG Pipeline
- Dataset Selection and Preparation
- Graph Indexing: Chunking Documents
- Entity and Relationship Extraction
- Entity and Relationship Summarization
- Community Detection and Summarization
- Graph Retrievers: Global Search
- Graph Retrievers: Local Search
Module 8: RAG Application Evaluation
8 lessons · Shu-Ha-Ri cycle
- Designing the Benchmark Dataset
- Coming Up with Test Examples
- Context Recall: Are You Finding the Right Information?
- Faithfulness: Is the LLM Using the Context?
- Answer Correctness: Is the Answer Right?
- Running Evaluation on Your System
- Observations and Next Steps
- Capstone: Your Production GraphRAG System
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
No. We cover Neo4j and Cypher from the ground up, including installation and basic queries. By the end, you'll be proficient in graph database operations.
Most RAG tutorials stop at vector similarity search. This course goes further—knowledge graphs, Cypher generation, agentic RAG, and the Microsoft GraphRAG pipeline. You'll build a production-quality system, not a toy demo.
Vector search finds similar content but misses relationships. GraphRAG captures entities, relationships, and context—enabling multi-hop reasoning, better traceability, and more accurate answers to complex questions.
Yes. You'll build a complete GraphRAG system with vector search, Cypher generation, agentic retrieval, and proper evaluation metrics. A real production system, not a tutorial project.
GraphRAG delivers better accuracy, traceability, and completeness than basic RAG. For enterprise applications where correctness matters—legal, medical, financial—the graph advantage is significant.
No. Everything runs on a standard laptop. We use Neo4j (free tier available) and cloud LLM APIs. No specialized hardware required.
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