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.

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.