Udemy – Advanced RAG: Build & Deploy Production GenAI Apps 2026-4

Udemy – Advanced RAG: Build & Deploy Production GenAI Apps 2026-4

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Description

Advanced RAG: Build & Deploy Production GenAI Apps. This course explores how to build and deploy advanced retrieval-augmentation systems for productive AI applications at production scale. Retrieval Augmentation (RAG) is at the core of all serious and practical AI applications today. However, basic and simple RAG pipelines quickly become limited when faced with large documents, complex queries, or the need for stable execution in a production environment. In this specialized course, learners learn how to implement a comprehensive production-grade retrieval-augmentation tool called RAGWire from scratch, built on the LangChain, Qdrant, and LangGraph frameworks. It starts with a simple hybrid search pipeline and progresses to advanced retrieval, metadata filtering, AI agent systems, multi-agent frameworks, a full conversational user interface, and cloud deployment. By the end of this course, participants will be able to design scalable and reliable AI systems that are capable of processing complex enterprise documents. In addition, the course shows developers how to manage the technical challenges associated with implementing AI in operational environments. By focusing on building functional and executable code, engineers can apply the learned patterns directly to their personal and organizational projects. Mastering modern cloud deployment tools and application security allows technical teams to bring AI-based products to market with the highest performance standards and ensure the sustainability of their infrastructure. This practical approach ensures that learners are fully prepared to enter the specialized AI job market.

What you will learn

Hybrid Pipeline Construction: Implement a hybrid RAG pipeline using BM25 sparse search, dense retrieval, and reciprocal rank fusion (RRF).
Configure models and embeddings: Set up the RAGWire tool with OpenAI GPT, Groq, Google Gemini, Ollama models, and HuggingFace embeddings.
AI-based metadata filtering: Implementing automated metadata filtering on complex and nested document structures.
Building Agent Systems: Creating agent RAG pipelines with LangChain agent tools, memory, and reasoning capabilities.
Self-healing agent design: Build a RAG agent that evaluates its retrieval quality and rewrites queries if the quality is low.
Implementing multi-agent systems: Building multi-agent supervisor systems using LangGraph to route queries to expert agents.
Document Analyzer Development: Building multi-agent document analyzers using CrewAI, Microsoft AutoGen, and the Microsoft Agent Framework.
Chat UI Design: Building a Chainlit-based chat UI with authentication, chat history, and document upload capabilities.
Backend development with FastAPI: Build a backend with OpenAI-compatible endpoints, SSE streaming, and testability with Postman.
Cloud deployment of agents: Deploy RAG agents on Render, Railway, AWS ECS Fargate, GCP Cloud Run, and Azure platforms.
Secure applications: Protect production APIs with access keys and secure authentication information using Docker.

This course is suitable for people who:

Python Developers: Programmers who want to go beyond basic training and implement RAG production systems.
Machine Learning Engineers: Professionals looking to deploy LangChain and LangGraph agents on AWS, GCP, or Azure cloud platforms.
Backend developers: Programmers who plan to build OpenAI-compatible endpoints for AI applications.
AI Engineers: Individuals looking to gain hands-on experience with CrewAI, AutoGen, and multi-agent systems frameworks.
Enterprise Systems Designers: Anyone working on building document search, enterprise AI assistants, or RAG agent applications.