Intro · 3:21
RAG fundamentals
Retrieval-augmented generation.
- RAG basics and components.
- From fundamentals to advanced.
- Hands-on.
| Part | Focus |
|---|---|
| Fundamentals | Concepts |
| Advanced | Query expansion |
Do this in your own tools, step by step. Ticks are saved on this device.
Topic?
RAG.
Path?
Basics to advanced.
0:00 this course will guide you through the basics of retrieval augmented generation or rag starting with its fundamental concepts and components you'll learn how to build a rag system for chatting with documents explore Advanced Techniques and understand the pitfalls of naive rag Paulo created this course he is a senior software engineer and experienced teacher in this video I'm going to go through a quick introduction of rag rag stands for retrieval augmented generation now if you have never heard of rag no worries that's what I'm going to be doing in this video the main idea is that when you use a large language model but a large language model essentially is a model that was trained on certain um data so for instance if you go to chat GPT and you type in what is the capital of France and of course it will give you the capital of France because it was trained on information about including in this case the capitals of countries in the world but if you were to ask chaj what is the name
1:11 of my first dog of course chaj wouldn't know because it's using that large language model the model that was trained on something that is not related to your information information that it is particular to you that is specific to you and that of course is an it a problem and and rag essentially allows us to take our own information our own data databases video textol information RW data or unstructured data as they call it sort of inject to the large language model so now the large language model has more information including your own information and so now when you ask questions related to your specific data you are able to get the answer from the large language model because it's able to connect to your data that you have injected happy day so you get the right answer so that is the idea of rag so that's what we're going to be doing this mini course or in this video and I hope you enjoy it all right let's go ahead and get started in order for you to follow along in this course you need to have your development environment setup particularly I expect you to of course have python setup on your machine also vs code or any other code editor of
2:25 your preference but I will be using vs code so I would encourage you to also use code but that is not a requirement also make sure that we have an open AI account which means uh you also need to create have an API key that way you're able to follow along if you want to actually do the Hands-On with me which I believe I want to believe that's what you're going to be doing so go ahead and have all those things set up and we should be good now for you to set up the open account again you can just go to open.com and go through the process if you haven't done that already ready and just set you up create an account and create an open API key which then we'll be using in this course and if you are wanting to install python you don't have python installed it's very simple just follow this link and they have all of the things or all of the directions you will need to set up python on your machine so I encourage you to go through that and have everything set up okay so they have python for Windows Mac Linux and everything this is all in case you don't have anything set up but go ahead and do that if you don't have that set
Get the other 13 chapters, the exam and your certificate for LinkedIn, plus all 700+ AI courses.