RAG Architecture Module¶

Welcome to the RAG Architecture module of the Agentic AI Nano-Degree! This comprehensive 10-session program takes you from RAG fundamentals to production-ready enterprise implementations.

Module Overview¶

This module provides complete coverage of Retrieval-Augmented Generation (RAG) systems, from basic concepts to advanced multi-modal and agentic implementations.

Duration: 10 Sessions
Time Investment: 50-140 minutes per session (depending on chosen path)
Prerequisites: Basic ML concepts, Python experience, understanding of vector spaces

Learning Journey¶

Foundation (Sessions 0-2)¶

Establish your understanding of RAG fundamentals and core implementation patterns.

Session 0: Introduction to RAG Architecture - RAG evolution from 2017 to 2025 - Fundamental architecture components - Problem-solving approaches and alternatives - Real-world use cases and applications

Session 1: Basic RAG Implementation - Core RAG pipeline development - Document indexing and retrieval - Generation and response synthesis - End-to-end system integration

Session 2: Advanced Chunking & Preprocessing - Intelligent document segmentation - Structure-aware chunking strategies - Metadata extraction and enrichment - Quality assessment and optimization

Search & Enhancement (Sessions 3-4)¶

Master sophisticated retrieval and query optimization techniques.

Session 3: Vector Databases & Search Optimization - Vector database selection and configuration - Hybrid search implementations - Index optimization strategies - Performance tuning and scaling

Session 4: Query Enhancement & Context Augmentation - Query understanding and expansion - Context-aware retrieval strategies - Multi-step reasoning approaches - Intent classification and routing

Evaluation & Quality (Session 5)¶

Learn to measure, monitor, and improve RAG system performance.

Session 5: RAG Evaluation & Quality Assessment - Comprehensive evaluation frameworks - Automated quality metrics - Human evaluation strategies - Continuous improvement processes

Advanced Architectures (Sessions 6-8)¶

Explore cutting-edge RAG implementations and specialized approaches.

Session 6: Graph-Based RAG - Knowledge graph integration - Entity relationship modeling - Graph traversal algorithms - Multi-hop reasoning systems

Session 7: Agentic RAG Systems - Agent-driven retrieval strategies - Self-improving RAG systems - Multi-agent RAG coordination - Autonomous quality control

Session 8: Multi-Modal Advanced RAG - Text, image, and audio integration - Cross-modal retrieval strategies - Multi-modal embedding techniques - Complex media processing

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¶

Select your engagement level for optimal learning:

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 →