Module 2: RAG Architecture¶

🎯📝⚙️ Learning Path Overview¶

This module offers three distinct learning paths for mastering Retrieval-Augmented Generation (RAG) systems:

🎯 Observer Path - Essential Concepts (2-3 hours total)¶

Outcome: Understand core RAG principles and when to use different approaches Best for: Managers, architects, and anyone needing conceptual understanding

📝 Participant Path - Practical Implementation (8-12 hours total)¶

Outcome: Build working RAG systems for production use Best for: Developers, data scientists, and hands-on practitioners

⚙️ Implementer Path - Complete Mastery (20-25 hours total)¶

Outcome: Deep expertise in advanced RAG architectures and optimization Best for: Senior developers, ML engineers, and RAG specialists

Session-by-Session Guide¶

🎯📝⚙️ Session 1: RAG Fundamentals & Vector Databases¶

🎯 Observer: Core concepts, when to use RAG, vector similarity basics (45 min) 📝 Participant: Build complete vector RAG system with Chroma/Pinecone (2 hours) ⚙️ Implementer: Advanced embedding strategies, custom vector operations (+ 2 hours)

Start Session 1 →

🎯📝⚙️ Session 2: Advanced Chunking & Query Processing¶

🎯 Observer: Why chunking matters, different chunking strategies (30 min) 📝 Participant: Implement semantic chunking, hierarchical structures (2 hours) ⚙️ Implementer: Custom chunking algorithms, performance optimization (+ 2 hours)

Start Session 2 →

🎯📝⚙️ Session 3: Search Enhancement & Query Expansion¶

🎯 Observer: Query expansion concepts, reranking benefits (30 min) 📝 Participant: Build query expansion, implement reranking systems (2.5 hours) ⚙️ Implementer: Advanced retrieval algorithms, custom ranking models (+ 2 hours)

Start Session 3 →

🎯📝⚙️ Session 4: Advanced Context & RAG Routing¶

🎯 Observer: Context engineering principles, routing strategies (30 min) 📝 Participant: Implement context routing, advanced prompting (2 hours) ⚙️ Implementer: Sophisticated routing algorithms, context optimization (+ 2 hours)

Start Session 4 →

🎯📝⚙️ Session 5: RAG Evaluation & Quality Assessment¶

🎯 Observer: Evaluation metrics, quality assessment approaches (30 min) 📝 Participant: Build comprehensive evaluation systems (2 hours) ⚙️ Implementer: Advanced metrics, automated evaluation pipelines (+ 2 hours)

Start Session 5 →

🎯📝⚙️ Session 6: Graph-Based RAG (GraphRAG)¶

🎯 Observer: GraphRAG concepts, when to use knowledge graphs (30 min) 📝 Participant: Build traditional and code GraphRAG systems (3 hours) ⚙️ Implementer: Advanced graph algorithms, production optimization (+ 8 hours)

Start Session 6 →

⚙️ Implementer Advanced Modules:¶

• ⚙️ NodeRAG Technical Implementation (3 hours)
• ⚙️ Code GraphRAG Advanced (2.5 hours)
• ⚙️ Graph Traversal Advanced (2.5 hours)
• ⚙️ Module A: Advanced Graph Algorithms (4 hours)
• ⚙️ Module B: Production GraphRAG (4 hours)

Production & Enterprise (Session 9)¶

Deploy and maintain enterprise-grade RAG systems.

Session 9: Production RAG & Enterprise Integration¶

• Scalable deployment architectures
• Security and compliance frameworks
• Monitoring and observability
• Enterprise workflow integration

Learning Paths by Use Case¶

Building Your First RAG System¶

Path: 🎯 Observer → 📝 Participant (Sessions 1-2) Time: 4-5 hours Outcome: Working RAG system for document Q&A

Production RAG System¶

Path: 📝 Participant (All Sessions) Time: 8-12 hours Outcome: Production-ready RAG with evaluation

Advanced RAG Architectures¶

Path: ⚙️ Implementer (All Sessions + Advanced Modules) Time: 20-25 hours Outcome: Expertise in cutting-edge RAG techniques

Code Analysis RAG¶

Path: 📝 Participant (Sessions 1-2) + ⚙️ Code GraphRAG Time: 6-7 hours Outcome: RAG system for codebase analysis

Enterprise Knowledge RAG¶

Path: 📝 Participant (All Sessions) + ⚙️ Production Modules Time: 15-18 hours Outcome: Enterprise-scale knowledge management RAG

Technical Stack¶

Core Technologies¶

• Vector Databases: Chroma, Pinecone, Weaviate, Qdrant
• Embedding Models: OpenAI, Sentence Transformers, Cohere
• LLMs: GPT-4, Claude, Llama, Gemini
• Search Engines: Elasticsearch, OpenSearch
• Graph Databases: Neo4j, Amazon Neptune

RAG Frameworks¶

• LangChain: RAG pipeline orchestration
• LlamaIndex: Advanced indexing and retrieval
• Haystack: Production RAG systems
• ChromaDB: Vector storage and retrieval
• FAISS: High-performance similarity search

Infrastructure¶

• Docker & Kubernetes: Containerization and orchestration
• Apache Kafka: Real-time data streaming
• Redis: Caching and session management
• PostgreSQL: Metadata and configuration storage
• Prometheus/Grafana: System monitoring

Session Structure¶

Each session follows a learner-optimized structure:

1. 🎯 Learning Navigation Hub
2. Path selection and time estimates
3. Skill prerequisites and outcomes

4. Quick start guidance

5. 📋 Session Overview Dashboard

6. Core learning track breakdown
7. Optional module previews

8. Time and complexity indicators

9. 🧭 Core Section (Required)

10. Essential RAG concepts
11. Progressive implementation

12. Practical code examples

13. 🔬 Optional Deep Dive Modules

14. Advanced techniques and optimizations
15. Enterprise-specific considerations

16. Research-level implementations

17. 📝 Knowledge Assessment

18. Comprehensive multiple choice tests
19. Practical implementation challenges

20. Concept validation exercises

21. 🧭 Navigation & Progression

22. Clear next steps and prerequisites
23. Related resource connections
24. Learning path continuity

Success Strategies¶

For Maximum Learning Impact¶

1. Progressive Mastery: Build on each session's concepts sequentially
2. Practical Implementation: Deploy examples in your own environment
3. Path Consistency: Maintain chosen learning depth for coherent experience
4. Iterative Refinement: Revisit complex concepts with fresh perspective
5. Real-World Application: Apply concepts to actual business problems

Recommended Schedule¶

• 👀 Observer Path: 2-3 sessions per week
• 🙋‍♂️ Participant Path: 1-2 sessions per week
• 🛠️ Implementer Path: 1 session per week with practice time
• Hybrid Approach: Adjust based on session complexity and available time

Learning Outcomes¶

Upon completion of this module, you will master:

RAG System Design¶

• Architect comprehensive RAG systems from requirements to deployment
• Select optimal components for specific use cases and constraints
• Design scalable, maintainable RAG architectures

Advanced Implementation¶

• Implement sophisticated retrieval strategies and query optimization
• Build multi-modal RAG systems handling diverse content types
• Create agentic RAG systems with self-improvement capabilities

Production Excellence¶

• Deploy enterprise-grade RAG systems with proper security and compliance
• Implement comprehensive monitoring, evaluation, and optimization workflows
• Integrate RAG systems into existing enterprise infrastructure

Quality & Evaluation¶

• Design and implement robust evaluation frameworks
• Monitor and improve system performance continuously
• Balance accuracy, latency, and cost considerations

🔗 Quick Navigation¶

Special Features¶

Three-Path Learning System¶

Each session accommodates different learning styles and time constraints while maintaining educational rigor and practical applicability.

Progressive Complexity¶

From basic RAG implementations to advanced multi-modal and agentic systems, building expertise systematically.

Enterprise Focus¶

Real-world considerations including security, compliance, monitoring, and integration with existing enterprise systems.

Cutting-Edge Content¶

Latest RAG techniques including graph-based approaches, agentic systems, and multi-modal implementations.

Ready to master RAG architecture? Start with the fundamentals and build towards production-ready implementations!

Begin: Session 0 - Introduction to RAG Architecture →
Curriculum: View Module Curriculum →