Fundamentals of AI Agents Using RAG and LangChain

Fundamentals of AI Agents Using RAG and LangChain

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

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.

Details to know

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Assessments

4 assignments

Taught in English

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There are 2 modules in this 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.

During this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll look at RAG, its applications, and its process, along with encoders, their tokenizers, and the FAISS library. Then, you’ll apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, you’ll explore LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs. Additionally, you’ll get valuable hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, you’ll complete a real-world project you can discuss in interviews. If you’re keen to boost your resume and extend your generative AI skills to applying transformer-based LLMs, ENROLL today and build job-ready skills in just 8 hours.

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

3 videosTotal 19 minutes

Course Introduction2 minutesPreview module
RAG7 minutes
RAG, Encoders, and FAISS9 minutes

3 readingsTotal 13 minutes

Course Overview 4 minutes
Specialization Overview7 minutes
Reading: Summary and Highlights 2 minutes

2 assignmentsTotal 24 minutes

Practice Quiz: Introduction to RAG9 minutes
Graded Quiz: RAG Framework15 minutes

2 app itemsTotal 120 minutes

Lab: RAG with Hugging Face60 minutes
Lab: RAG with PyTorch60 minutes

1 pluginTotal 1 minute

Helpful Tips for Course Completion1 minute

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

6 videosTotal 33 minutes

Introduction to LangChain4 minutesPreview module
Introduction to In-context Learning5 minutes
Advanced Methods of Prompt Engineering5 minutes
LangChain: Core Concepts6 minutes
LangChain Documents for Building RAG Applications4 minutes
LangChain Chains and Agents for Building Applications6 minutes

4 readingsTotal 9 minutes

Summary and Highlights3 minutes
Course Conclusion2 minutes
Congratulations and Next Steps2 minutes
Thanks from the Course Team2 minutes

2 assignmentsTotal 39 minutes

Practice Quiz: Prompt Engineering and LangChain18 minutes
Graded Quiz: Prompt Engineering and LangChain21 minutes

3 app itemsTotal 130 minutes

Lab: In-Context Engineering and Prompt Templates30 minutes
Lab: LangChain40 minutes
Guided Project: Summarize Private Documents Using RAG, LangChain, and LLMs60 minutes

2 pluginsTotal 30 minutes

Cheat Sheet: Fundamentals of Building AI Agents using RAG and LangChain15 minutes
Glossary: Fundamentals of Building AI Agents using RAG and LangChain15 minutes