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LangChain - Develop Controlled AI Agent with LangChain & RAG
Published 12/2025
Duration: 4h 2m | .MP4 1920x1080 30fps(r) | AAC, 44100Hz, 2ch | 2.12 GB
Genre: eLearning | Language: English
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 deploycontrolled Business AI AgentsusingLangChain,RAG (Retrieval-Augmented Generation),OpenAI LLMs, and a production-ready backend withFastAPI.
This course focuses on how real AI agent systems are structured in modern products and startups. You will learn how to combineagents, chains, prompts, schemas, and vector databasesto create AI systems that can reason, plan, retrieve knowledge, and validate outputs in a controlled and reliable way.
*** 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
More Info
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