Business demand for technical gen AI skills is exploding, and 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.
Fundamentals of AI Agents Using RAG and LangChain
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Fundamentals of AI Agents Using RAG and LangChain
This course is part of multiple programs.
Instructors: Joseph Santarcangelo +3 more
16,647 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.6
(102 reviews)
Intermediate level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
In-demand, job-ready skills businesses seek for building AI agents using RAG and LangChain in just 8 hours
How tapply the fundamentals of in-context learning and advanced prompt engineering timprove prompt design
Key LangChain concepts, including tools, components, chat models, chains, and agents
How tbuild AI applications by integrating RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies
Skills you'll gain
- Category: Artificial Intelligence
- Category: Prompt Engineering
- Category: Large Language Modeling
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: ChatGPT
- Category: Natural Language Processing
- Category: OpenAI
- Category: Generative AI
- Category: Generative AI Agents
Details to know
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Assessments
4 assignments
Taught in English
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There are 2 modules in this course
In this module, you will explore the fundamentals of retrieval-augmented generation (RAG) and how it is applied to generate more accurate and context-aware responses in applications such as chatbots and intelligent AI agents. You will learn about the complete RAG process, including its integration with LangChain for building modular and scalable AI solutions. The module covers key components such as dense passage retrieval (DPR), which uses a context encoder and a question encoder, each paired with tokenizers to convert text into a machine-readable format. It also introduces the Facebook AI similarity search (FAISS) library, developed by Facebook AI Research, for performing efficient similarity searches in high-dimensional vector spaces. Additionally, you will gain hands-on experience through labs that focus on implementing RAG-based systems using two major machine learning frameworks: Hugging Face, for retrieving information from datasets, and PyTorch, for evaluating content relevance and generating meaningful responses.
What's included
3 videos3 readings2 assignments2 app items1 plugin
In this module, you will learn about in-context learning and advanced prompt engineering techniques to design and refine prompts for generating relevant and accurate AI responses. You’ll then explore the LangChain framework, an open-source interface that simplifies AI application development using large language models (LLMs). The key concepts covered include LangChain’s tools, components, and chat models, as well as prompt templates, example selectors, and output parsers. You’ll also examine LangChain’s document loader and retriever, chains, and agents to build intelligent applications. Through hands-on labs, you’ll apply these concepts to enhance LLM applications and develop an AI agent that integrates LLM, LangChain, and RAG for interactive and efficient document retrieval. Additionally, a comprehensive cheat sheet and glossary are available to reinforce your learning.
What's included
6 videos4 readings2 assignments3 app items2 plugins
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Instructors
Instructor ratings
4.2 (14 ratings)Joseph Santarcangelo
IBM
35 Courses1,982,745 learners
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IBM
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4.6
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Showing 3 of 102
HB
4
Reviewed on Apr 26, 2025
Course content was good but there was not much for us to do in labs. A hint based lab completely solved by the learner can be a good addition.
MS
5
Reviewed on Apr 27, 2025
An amazing course. A little fast paced but fulfills its purpose of delivering the knowledge in a such a short span of time.
MM
5
Reviewed on Feb 9, 2025
The hands-on is manageable, yet allow learners to experience the actual flow of using the tools.
Frequently asked questions
With 3-4 hours of study, you can complete this course and build the job-ready skills you need to impress an employer within just eight hours!
This course is intermediate level, so to get the most out of your learning, you must have basic knowledge of Python and PyTorch. You should also be familiar with machine learning and neural network concepts, and it is helpful if you are familiar with language modeling, transformer models, GPT, and fine-tuning fundamentals.
This course is part of the Generative AI Engineering with LLMs specialization. When you complete this course, you will have the skills and confidence to take on jobs such as AI engineer, NLP engineer, machine learning engineer, deep learning engineer, data scientist, or software seeking to work with LLMs.