LangChain – Develop Controlled AI Agent with LangChain & RAG

Master LangChain & RAG (Retrieval-Augmented Generation) to build controlled Business AI Agent with OpenAI LLMs

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

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 AgentsWhy LangChain is used for agent orchestrationHow to design controlled AI agents for business use casesPrompt engineering for business, planning, marketing, emails, and tasksUsing schemas to enforce structured AI responsesBuilding chains and agent executorsUnderstanding RAG (Retrieval-Augmented Generation) in depthUploading files and converting them into usable AI contextCreating embeddings and storing them in a vector databasePerforming similarity search using retrieversManaging context and solving RAG memory issuesReviewing and validating AI responses before final outputViewing and managing vectors in ChromaDBAdding security middleware to your AI backendRunning 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 requirementsA Planning Agent for structured decision-makingA Marketing Agent for strategy and content generationAn Email Agent for professional communicationA Tasks Agent for structured task generationA RAG (Retrieval-Augmented Generation) pipeline using a vector databaseResponse review and validation before final outputA backend API built with FastAPIBy the end of the course, you will understand how multiple agents work together in a real-world AI system.

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