LangChain - Develop Controlled AI Agent with LangChain & RAG

About this course

Learn how to design, build, and deploy controlled Business AI Agents using LangChain, RAG (Retrieval-Augmented Generation), OpenAI LLMs, and a production-ready backend with FastAPI.This course focuses on how real AI agent systems are structured in modern products and startups. You will learn how to combine agents, chains, prompts, schemas, and vector databases to create AI systems that can reason, plan, retrieve knowledge, and validate outputs in a controlled and reliable way.* What You Will Learn *The difference between LLMs and AI Agents

Why LangChain is used for agent orchestration

How to design controlled AI agents for business use cases

Prompt engineering for business, planning, marketing, emails, and tasks

Using schemas to enforce structured AI responses

Building chains and agent executors

Understanding RAG (Retrieval-Augmented Generation) in depth

Uploading files and converting them into usable AI context

Creating embeddings and storing them in a vector database

Performing similarity search using retrievers

Managing context and solving RAG memory issues

Reviewing and validating AI responses before final output

Viewing and managing vectors in ChromaDBAdding security middleware to your AI backend

Running the complete AI agent using FastAPI* Project You Will Build *In this course, you will build a complete Business AI Agent system that includes:

• A Business Agent for understand

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.