Overview

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!

Syllabus

Fundamentals of AI Agents

In this module, you’ll explore what AI agents are, how they differ from traditional AI systems, and when it’s appropriate to use them. You’ll learn the basics of tool calling and how it enables AI agents to interact with external systems. Through a hands-on lab, you’ll build a simple AI agent from scratch and understand the benefits and limitations of agent-based approaches in real-world applications.

RAG Framework

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.

Prompt Engineering and LangChain

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.