Advanced RAG: Build & Deploy Production GenAI Apps

<div>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.</div><div><br></div><div>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.</div><div><br></div><div><span style="font-size: 1rem;">By the end of this course you will know how to:</span></div><div><ul><li><span style="font-size: 1rem;">Build a hybrid RAG pipeline with BM25 sparse + dense retrieval and Reciprocal Rank Fusion (RRF)</span></li><li><span style="font-size: 1rem;">Configure RAGWire with OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings</span></li><li><span style="font-size: 1rem;">Implement LLM-driven auto metadata filtering over complex, nested document structures</span></li><li><span style="font-size: 1rem;">Build agentic RAG pipelines with LangChain agent tools, memory, and reasoning</span></li><li><span style="font-size: 1rem;">Build a self-correcting RAG agent that grades its own retrieval and rewrites queries when quality is low</span></li><li><span style="font-size: 1rem;">Build supervisor multi-agent systems that route queries to specialist agents using LangGraph</span></li><li><span style="font-size: 1rem;">Build multi-agent document analysts with CrewAI, Microsoft AutoGen, and Microsoft Agent Framework</span></li><li><span style="font-size: 1rem;">Build a production Chainlit chat UI with authentication, chat history, and document upload</span></li><li><span style="font-size: 1rem;">Build a FastAPI backend with OpenAI-compatible /v1/chat/completions endpoints and SSE streaming</span></li><li><span style="font-size: 1rem;">Deploy RAG agents to Render, Railway, AWS ECS Fargate, GCP Cloud Run, and Azure</span></li><li><span style="font-size: 1rem;">Secure production APIs with API keys and protect credentials with Docker .dockerignore</span></li></ul></div><div><span style="font-size: 1rem;">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.</span></div>

What you'll learn:

• Build a production RAG pipeline with BM25 hybrid search, RRF fusion, and Qdrant vector database
• Build agentic RAG systems with LangChain, LangGraph self-correcting agents, and supervisor workflows
• Build multi-agent RAG with CrewAI, Microsoft AutoGen, and Microsoft Agent Framework
• Deploy RAG agents to AWS ECS Fargate, GCP Cloud Run, Azure, Railway, and Render with Docker
• Build a FastAPI backend with OpenAI-compatible endpoints, SSE streaming, and Postman testing
• Build a production Chainlit chat UI with authentication, chat history, and document ingestion
• Configure RAGWire with OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings
• Implement LLM-driven auto metadata filtering over complex nested document structures in Qdrant