AI Agents Fundamentals - LLMs, LangChain, RAG, and Vector Databases

Learn AI agent development from the ground up in this comprehensive 57-minute tutorial that requires no prior knowledge and takes you from basic concepts to building production-ready AI systems. Master fundamental AI concepts including how Large Language Models (LLMs) work in real-time, understand tokens, embeddings, and context windows, then dive into practical development with LangChain's pre-built components that simplify AI application creation. Explore advanced prompt engineering techniques including zero-shot, few-shot, and chain-of-thought approaches, then implement semantic search capabilities using vector databases like ChromaDB and Pinecone. Build intelligent document search systems using RAG (Retrieval Augmented Generation), create sophisticated multi-step AI workflows with LangGraph, and connect AI systems to external tools through the Model Context Protocol (MCP). Follow along with hands-on labs that include making your first OpenAI API calls, building semantic search engines, creating RAG systems for document retrieval, developing multi-agent workflows, and integrating external tools, culminating in building a real-world AI assistant capable of searching 500GB of documents in under 30 seconds.