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
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
Sponsored by InternMart, Inc
2,739 already enrolled
(21 reviews)
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What you'll learn
In-demand job-ready skills businesses need for building AI agents using RAG and LangChain in just 8 hours.
How to apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.
Key LangChain concepts, tools, components, chat models, chains, and agents.
How to apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies to different applications.
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4 assignments
September 2024
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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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