Build job-ready skills for AI agents in just 2 weeks. Plus, valuable practical experience and a credential. Familiarity with RAG and LangChain.
This Fundamentals of AI Agents Using RAG and LangChain course teaches you the skills you need for your AI career.
You will learn:
- Job-ready skills in 2 weeks, plus you’ll get practical experience employers look for on a resume and an industry-recognized credential
- Apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design
- Explore key concepts of LangChain, LangChain tools, components, chat models, chains, and agents
- Apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies for different applications
Course Overview
Business demand for technical generative AI skills is exploding and generative 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 just 2 weeks.
In this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll look at RAG, its applications, processes, encoders, tokenizers, and the Facebook AI Similarity Search (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 for applying transformer-based LLMs, ENROLL today and build job-ready skills in 8 hours.
Course Syllabus
Module 0: Welcome
· Video: Course Introduction
· Reading:General Information
· Reading:Learning Objectives and Syllabus
· Reading:Grading Scheme
· Reading: Specialization Overview
· Reading: Helpful Tips for Course Completion
Module 1: RAG Framework
· Reading: Module 1 Introduction and Learning Objectives
· Video: RAG
· Video: RAG, Encoders, and FAISS
· Lab: RAG with Hugging Face
· Lab: RAG with PyTorch
· Practice Quiz: Module 1: RAG Framework
· Reading:Summary and Highlights
· Graded Quiz: RAG Framework
Module 2: Prompt Engineering and LangChain
· Reading: Module 2 Introduction and Learning Objectives
· Video: Introduction to LangChain
· Video: Introduction to In-context Learning
· Video: Advanced Methods of Prompt Engineering
· Lab: In-Context Engineering and Prompt Templates
· Video: LangChain: Core Concepts
· Video: LangChain Documents for Building RAG Applications
· Video: LangChain Chains and Agents for Building Applications
· Lab: LangChain
· Lab: Guided Project: Summarize Private Documents Using RAG, LangChain, and LLMs
· Practice Quiz: Prompt Engineering and LangChain
· Reading: Summary and Highlights: Prompt Engineering and LangChain
· Graded Quiz: Prompt Engineering and LangChain
· Reading: Cheat Sheet: Fundamentals of Building AI Agents using RAG and LangChain
· Reading: Glossary: Fundamentals of Building AI Agents using RAG and LangChain
· Reading: Course Conclusion
· Reading: Congratulations and Next Steps
· Reading: Thanks from the Course Team
· Reading: Copyrights and Trademarks
Course Rating and Feedback
· Feedback
Badge
· How to claim your certificate?
· Reading: Claim Your Badge Here
Recommended Skills Prior to Taking this Course
Basic knowledge of generative AI, prompt engineering techniques, and working knowledge of machine learning with Python and PyTorch.