RAG (Retrieval-Augmented Generation)
Learn how to build intelligent systems that combine your documents with LLM reasoning for accurate, contextual responses.Getting Started with RAG
- Basic RAG Implementation - Build a simple RAG system
- Agentic RAG - Autonomous agents with retrieval strategies
- Contextual RAG - Advanced multi-turn conversation handling
Advanced Implementation Patterns
Explore sophisticated AI application architectures and integration patterns.Web Integration & Real-time Data
- Web Search + LLM - Combine web search with language models
- Data Preprocessor Agent - Build intelligent data analysis agents
Key Concepts Covered
RAG Systems- Vector database integration and embedding generation
- Context retrieval strategies and response synthesis
- Performance optimization and error handling
- Agent architecture patterns and function calling
- Multi-step reasoning workflows and tool integration
- Autonomous decision-making and task execution
- Document parsing and preprocessing techniques
- Multi-modal data analysis and real-time integration
- Scalable data pipeline design
Each guide includes complete code examples, best practices, and production-ready implementations you can use as starting points for your own projects.