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Fundamentals of AI Agents using RAG and LangChain

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IntermediateCourse

Immerse yourself in AI, exploring skills like RAG and in-context learning. This course guides you in building chatbots, leveraging LangChain, and using vector databases. With intermediate-level focus, it deepens your understanding of AI agents. Enhance your proficiency and transform ideas into intelligent solutions.

4.6 (201 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Artificial Intelligence

Enrollment Count

  • 41.88K

Skills You Will Learn

  • Chatbots, In-Context Learning, LangChain, RAG, Vector Databases

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 Weeks/ 2hrs

Platform

  • Coursera

Last Update

  • March 17, 2026
About this Course
This Fundamentals of AI Agents Using RAG and LangChain course teaches you the skills you need for your AI career.  
 
You will learn: 
  • Job-ready skills in 2 weeks, plus you’ll get practical experience employers look for on a resume and an industry-recognized credential 
  • Apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design 
  • Explore key concepts of LangChain, LangChain tools, components, chat models, chains, and agents 
  • Apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies for different applications 
 
Course Overview 
 
Business demand for technical generative AI skills is exploding and generative AI engineers who can work with large language models (LLMs) are in high demand. This Fundamentals of Building AI Agents using RAG and LangChain course builds job-ready skills that will fuel your AI career in just 2 weeks.   
 
In this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll look at RAG, its applications, processes, encoders, tokenizers, and the Facebook AI Similarity Search (Faiss) library. Then, you’ll apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll explore LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs.  

Additionally, you’ll get valuable hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, you’ll complete a real-world project you can discuss in interviews. 
 
If you’re keen to boost your resume and extend your generative AI skills for applying transformer-based LLMs, ENROLL today and build job-ready skills in 8 hours. 

Course Syllabus

Module 1: RAG Framework 
  • Reading: Module Introduction and Learning Objectives 
  • Video: RAG 
  • Video: RAG, Encoders, and FAISS 
  • Lab: RAG with Hugging Face 
  • Lab: RAG with PyTorch 
  • Practice Quiz: Module 1: RAG Framework 
  • Reading: Summary and Highlights  
  • Graded Quiz: RAG Framework
Module 2: Prompt Engineering and LangChain 
  • Reading: Module Introduction and Learning Objectives 
  • Video: Introduction to LangChain 
  • Video: Introduction to In-context Learning 
  • Video: Advanced Methods of Prompt Engineering 
  • Lab: In-Context Engineering and Prompt Templates 
  • Video: LangChain: Core Concepts 
  • Video: LangChain Documents for Building RAG Applications 
  • Video: LangChain Chains and Agents for Building Applications 
  • Lab: LangChain 
  • Lab: Guided Project: Summarize Private Documents Using RAG, LangChain, and LLMs 
  • Practice Quiz: Prompt Engineering and LangChain 
  • Reading: Summary and Highlights: Prompt Engineering and LangChain 
  • Graded Quiz: Prompt Engineering and LangChain 
  • Reading: Cheat Sheet: Fundamentals of Building AI Agents using RAG and LangChain 
  • Reading: Glossary: Fundamentals of Building AI Agents using RAG and LangChain 

Recommended Skills Prior to Taking this Course

Basic knowledge of generative AI, prompt engineering techniques, and working knowledge of machine learning with Python and PyTorch.