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
15,742 already enrolled
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4.6
(97 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: Large Language Modeling
- Category: Application Development
- Category: Generative AI
- Category: Artificial Intelligence
- Category: Generative AI Agents
- Category: Prompt Engineering
- Category: Artificial Intelligence and Machine Learning (AI/ML)
- Category: Natural Language Processing
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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 learn how RAG is used to generate responses for different applications such as chatbots. You’ll then learn about the RAG process, the Dense Passage Retrieval (DPR) context encoder and question encoder with their tokenizers, and the Faiss library developed by Facebook AI Research for searching high-dimensional vectors. In hands-on labs, you will use RAG with PyTorch to evaluate content appropriateness and with Hugging Face to retrieve information from the dataset.
What's included
3 videos3 readings2 assignments2 app items1 plugin
In this module, you will learn about in-context learning and advanced methods of prompt engineering to design and refine the prompts for generating relevant and accurate responses from AI. You’ll then be introduced to the LangChain framework, which is an open-source interface for simplifying the application development process using LLM. You’ll learn about its tools, components, and chat models. The module also includes concepts such as prompt templates, example selectors, and output parsers. You’ll then explore the LangChain document loader and retriever, LangChain chains and agents for building applications. In hands-on labs, you will enhance LLM applications and develop an agent that uses integrated LLM, LangChain, and RAG technologies for interactive and efficient document retrieval.
What's included
6 videos4 readings2 assignments3 app items2 plugins
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Instructors
Instructor ratings
4.1 (11 ratings)Joseph Santarcangelo
IBM
35 Courses1,966,408 learners
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IBM
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4.6
97 reviews
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Showing 3 of 97
HB
4
Reviewed on Apr 25, 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.
MM
5
Reviewed on Feb 8, 2025
The hands-on is manageable, yet allow learners to experience the actual flow of using the tools.
AM
4
Reviewed on Mar 14, 2025
The robotic voice of the reader made the experience a little fake, but the content was interesting
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.