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Learner Reviews & Feedback for Fundamentals of AI Agents Using RAG and LangChain by IBM

4.6

stars

149 ratings

About the Course

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. In this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll learn about the RAG process, its applications, encoders and tokenizers, and the FAISS library for high-dimensional vector search. Then, you’ll apply in-context learning and advanced prompt engineering techniques, including prompt templates and example selectors, to generate accurate responses. You’ll also work with LangChain’s tools, components, document loaders, retrievers, chains, and agents to simplify LLM-based application development. Through hands-on labs, you’ll develop AI agents that integrate LLMs, LangChain, and RAG technologies. You will also complete a real-world project you can showcase in interviews. A comprehensive cheat sheet and glossary are included to reinforce your learning. Enroll today and build in-demand generative AI skills in just 8 hours!...

Top reviews

Sep 29, 2025

Interesting, engaging, with easy to follow python code samples and explanations. Nice AI coach can help you to repeat the material or explain complex ideas. Enjoyable experience :)

Aug 23, 2025

Descriptive Course. I think some Labs need to update to latest libraries because some functions getting failed or generate warnings

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26 - 31 of 31 Reviews for Fundamentals of AI Agents Using RAG and LangChain

By Laxman G

•

Aug 28, 2024

A glossary would be useful.

By Bevan J

•

Nov 30, 2024

I think there are some issues with the tests - a number of times I feel the answers are either incorrect or the questions are poorly worded so as to be ambiguous. For example: "In agents, a language model is used as a reasoning engine to determine which of the following actions?" - LangChain agents use only Python code for building applications - Language model - Task manager - Data loader From the videos/summary, agents are clearly communicated as Task manager (see below). Additionally, the wording 'In agents' feels like it is referring to a field or domain of specialization which was never introduced; the concept of an agent as an object was taught. Furthermore, the grammar of the question and answers don't align - by asking for 'actions' you are asking for a verb, but all of the answers are nouns. " - Agents in LangChain are dynamic systems where a language model determines and sequences actions, such as predefined chains. - Agents integrate with tools such as search engines, databases, and websites to fulfill user requests. " Furthermore, an LLM by definition cannot be referred to as a reasoning engine as it is probabilistic by its very nature. The ability to reason (or produce reasoned responses) is not the same as a reasoning engine, which is something like a calculator that is deterministic and based on fundamental axioms.