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LangChain - Develop Controlled AI Agent with LangChain & RAG
Role Play
Rating: 4.7 out of 5(2 ratings)
39 students

LangChain - Develop Controlled AI Agent with LangChain & RAG

Master LangChain & RAG (Retrieval-Augmented Generation) to build controlled Business AI Agent with OpenAI LLMs
Role Play
Created byGhulam Abbas
Last updated 1/2026
English

What you'll learn

  • Understand the difference between LLMs and AI Agents
  • Learn how LangChain is used to build structured, multi-agent systems
  • Design and build a Business AI Agent from scratch
  • Use schemas to enforce structured and predictable AI outputs
  • Build reusable chains and manage execution with agent executors
  • Develop specialized agents for planning, marketing, emails, and tasks
  • Control agent decision-making and reduce hallucinations
  • Implement RAG (Retrieval-Augmented Generation) step by step
  • Convert documents into AI-readable knowledge using embeddings
  • Store and retrieve context using a vector database
  • Perform similarity search to provide relevant context to AI agents
  • Manage and clear RAG memory to avoid stale or incorrect responses
  • Review and validate AI outputs before delivering final results
  • Build and serve your AI agent using FastAPI
  • Add basic security middleware to protect AI endpoints

Course content

14 sections • 38 lectures • 4h 2m total length

  • Introduction to Course1:14

Requirements

  • Basic coding concepts are needed
  • Familiar with subjects such as: python, environment variables, classes

Description

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 ChromaDB

  • Adding 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 understanding requirements

  • A Planning Agent for structured decision-making

  • A Marketing Agent for strategy and content generation

  • An Email Agent for professional communication

  • A Tasks Agent for structured task generation

  • A RAG (Retrieval-Augmented Generation) pipeline using a vector database

  • Response review and validation before final output

  • A backend API built with FastAPI


By the end of the course, you will understand how multiple agents work together in a real-world AI system.

Who this course is for:

  • Anyone who want to learn how to build AI agents with LangChain and RAG
  • Anyone who wants to learn LangChain
  • Anyone who wants to learn about controlled AI Agents

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