Fundamentals of AI Agents Using RAG and LangChain

IBM via Coursera

Coursera based on 90 ratings

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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. During this course, youll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. Youll look at RAG, its applications, and its process, along with encoders, their tokenizers, and the FAISS library. Then, youll apply in-context learning...

Syllabus

RAG Framework

In this module, you will learn how RAG is used to generate responses for different applications such as chatbots. Youll 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.

Prompt Engineering and LangChain

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. Youll then be introduced to the LangChain framework, which is an open-source interface for simplifying the application development process using LLM. Youll learn about its tools, components, and chat models. The module also includes concepts such as prompt templates, example selectors, and output parsers. Youll 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.