LangChain – Develop Controlled AI Agent with LangChain & RAG

LangChain – Develop Controlled AI Agent with LangChain & RAG
Published 12/2025
Duration: 4h 2m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 2.12 GB

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
Description
Learn how to design, build, and deploycontrolled Business AI AgentsusingLangChain,RAG (Retrieval-Augmented Generation),OpenAI LLMs, and a production-ready backend withFastAPI.

*** What You Will Learn ***

The difference betweenLLMs and AI Agents

WhyLangChainis used for agent orchestration

How to designcontrolled AI agentsfor business use cases

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

Usingschemasto enforce structured AI responses

Buildingchains and agent executors

UnderstandingRAG (Retrieval-Augmented Generation)in depth

Uploading files and converting them into usable AI context

Creating embeddings and storing them in avector database

Performing similarity search using retrievers

Managing context and solvingRAG memory issues

Reviewing and validating AI responses before final output

Viewing and managing vectors inChromaDB

Adding security middleware to your AI backend

Running the complete AI agent usingFastAPI

*** Project You Will Build ***

In this course, you will build acomplete Business AI Agent systemthat 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

ARAG (Retrieval-Augmented Generation)pipeline using a vector database

Response review and validation before final output

A backend API built withFastAPI

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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