Build Context-Aware AI Applications with LangChain, RAG, and Vector Databases
Retrieval Augmented Generation (RAG) solves a core limitation of large language models: they cannot access your private data or documents. This track teaches you to combine LangChain's orchestration layer, embedding models, and vector databases so that your applications can retrieve the right context at query time and feed it to an LLM — producing accurate, grounded answers instead of hallucinations. These skills are directly applicable to chatbots over internal documentation, semantic search engines, and production AI pipelines.
What You Will Learn
You will start with how LLMs and RAG work conceptually, then move into LangChain's core abstractions: chains, prompts, and memory. From there you will cover document loading, text chunking, and the full embedding pipeline that turns raw text into vectors stored in a vector database. You will build and evaluate a complete RAG application, customize LangChain components, and address security and ethical risks specific to retrieval systems. Advanced modules cover hybrid and dense retrieval strategies, the internal architectures of vector databases like Pinecone, Weaviate, and Chroma, and the operational concerns — latency, cost, monitoring — involved in shipping RAG to production. The track closes with agentic RAG patterns where the retriever itself becomes a tool an LLM can call dynamically.
The Learning Path
Twelve courses progress from A1 through C2. The first two courses (A1–A2) establish foundations: LLM and RAG concepts, then LangChain's building blocks. The B-level courses (B1–B2) cover document processing, embeddings, vector database fundamentals, a full RAG build-and-evaluate cycle, component customization, and security considerations. The C1 courses move into advanced retrieval techniques and a deep look at vector database architectures, ending with Productionizing RAG Systems. The two C2 capstones — Agentic RAG and Tool Integration and Advanced RAG Use Cases and Future Trends — cover multi-step agent workflows and emerging patterns at the frontier of applied LLM engineering.
How It Works
Each course is split into short, focused lessons you complete in the built-in code editor with real-time feedback. An AI tutor is available when you get stuck, and exercises are grounded in realistic scenarios — loading real documents, querying live vector stores, and debugging retrieval pipelines — so the skills transfer directly to production work.
