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
Fundamentals of AI Agents Using RAG and LangChain
This course is part of Generative AI Engineering with LLMs Specialization
Taught in English
Instructors: Joseph Santarcangelo
Included with 
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
6 hours (approximately)
Flexible schedule
Learn at your own pace
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.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
4 assignments
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
6 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Generative AI Engineering with LLMs Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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Add this credential to your LinkedIn profile, resume, or CV
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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
Offered by
Recommended if you're interested in Machine Learning
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Frequently asked questions
With 3-4 hours of study, you can complete this course and build the job-ready skills you need to impress an employer within just eight hours!
This course is intermediate level, so to get the most out of your learning, you must have basic knowledge of Python and PyTorch. You should also be familiar with machine learning and neural network concepts, and it is helpful if you are familiar with language modeling, transformer models, GPT, and fine-tuning fundamentals.
This course is part of the Generative AI Engineering with LLMs specialization. When you complete this course, you will have the skills and confidence to take on jobs such as AI engineer, NLP engineer, machine learning engineer, deep learning engineer, data scientist, or software seeking to work with LLMs.