🔍 RAG & Knowledge Systems
Grounding AI agents with intelligent retrieval
Welcome to the RAG (Retrieval Augmented Generation) section - where you’ll learn to build AI systems that combine the power of large language models with the precision of knowledge retrieval.
What You’ll Learn
Section titled “What You’ll Learn”This section takes you from RAG basics to advanced graph-based knowledge systems:
📚 RAG Fundamentals
Section titled “📚 RAG Fundamentals”Start with the core concepts of Retrieval Augmented Generation. Learn how to ground AI responses in factual, up-to-date information from your knowledge base.
🤖 Agentic RAG
Section titled “🤖 Agentic RAG”Discover how to add active reasoning to knowledge search. Agentic RAG systems don’t just retrieve - they think about what to search for and how to use the results.
🕸️ GraphRAG
Section titled “🕸️ GraphRAG”Master relationship-based knowledge graphs that understand connections between concepts. GraphRAG excels at complex queries that require understanding context and relationships.
⚓ Grounding Techniques
Section titled “⚓ Grounding Techniques”Learn advanced strategies for grounding AI responses in reality. Prevent hallucinations and ensure factual accuracy through sophisticated grounding methods.
Learning Path
Section titled “Learning Path”Progress through these guides in order:
- RAG Fundamentals - Core concepts and basic implementation
- Agentic RAG - Adding intelligence to retrieval
- GraphRAG - Relationship-based knowledge systems
- Grounding Techniques - Advanced accuracy strategies
🌱 → 🌿 → 🌳 Progressive Learning
Section titled “🌱 → 🌿 → 🌳 Progressive Learning”Each guide builds on the previous:
- 🌱 Seedling - Simple RAG concepts for beginners
- 🌿 Sprout - Working implementations with code
- 🌳 Forest - Production-scale RAG systems
- 💡 Insight - Key architectural decisions
- ⚡ Quick Win - Minimal viable RAG in minutes
- 🔬 Deep Dive - Cutting-edge research
Why RAG Matters
Section titled “Why RAG Matters”RAG solves critical AI challenges:
- ✅ Reduces hallucinations - Ground responses in facts
- ✅ Stays current - Access up-to-date information
- ✅ Adds expertise - Incorporate domain knowledge
- ✅ Improves accuracy - Verify claims with sources
- ✅ Enables transparency - Show where information comes from
Prerequisites
Section titled “Prerequisites”Helpful background knowledge:
- Basic understanding of LLM agents
- Familiarity with Memory Management
- General AI/ML concepts
Who This Is For
Section titled “Who This Is For”📖 Content Creators - Build AI that understands your knowledge base
🏢 Enterprise Teams - Create internal knowledge assistants
🔬 Researchers - Ground AI in academic literature
💼 Product Builders - Add intelligent search to your apps
Real-World Applications
Section titled “Real-World Applications”RAG systems you’ll build:
- 📚 Document Q&A assistants
- 🏢 Internal knowledge bases
- 🔍 Research assistants
- 📊 Data analysis tools
- 💬 Customer support bots
- 🎓 Educational tutors
Next Steps
Section titled “Next Steps”Ready to build smarter AI? Start with RAG Fundamentals to understand the basics.
Part of the HUB Cookbooks by CURATIONS