Advanced RAG: Build & Deploy Production GenAI Apps

Retrieval-Augmented Generation (RAG) is at the core of every serious AI application today. But basic RAG pipelines quickly hit their limits when documents are large, queries are complex, or your application needs to run reliably in production. In this course, you will build RAGWire — a production-grade RAG toolkit built on LangChain, Qdrant, and LangGraph — from the ground up. You will start with a simple hybrid search pipeline and progressively add advanced retrieval, metadata filtering, agentic RAG, multi-agent frameworks, a full chat UI, and multi-cloud deployment. By the end of this course you will know how to: Build a hybrid RAG pipeline with BM25 sparse + dense retrieval and Reciprocal Rank Fusion (RRF) Configure RAGWire with OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings Implement LLM-driven auto metadata filtering over complex, nested document structures Build agentic RAG pipelines with LangChain agent tools, memory, and reasoning Build a self-correcting RAG agent that grades its own retrieval and rewrites queries when quality is low Build supervisor multi-agent systems that route queries to specialist agents using LangGraph Build multi-agent document analysts with CrewAI, Microsoft AutoGen, and Microsoft Agent Framework Build a production Chainlit chat UI with authentication, chat history, and document upload Build a FastAPI backend with OpenAI-compatible /v1/chat/completions endpoints and SSE streaming Deploy RAG agents to Render, Railway, AWS ECS Fargate, GCP Cloud Run, and Azure Secure production APIs with API keys and protect credentials with Docker .dockerignore This is a hands-on, code-first course. Every section produces working, runnable code that you can adapt to your own documents and use cases.