What RAG is

0:00 Uh we'll be talking about the entire pipeline from data injection to retrieval pipeline to output generation. how to use LLM models, how to use embedding models in this uh along with this uh what should be the right strategy of using chunkings and many more things right so we will be deep diving into both the theoretical understanding along with the practical implementation and we will initially go ahead step by step we'll start with the basic implementation and then as we go ahead in the advanced section we'll also implement the modular coding right the main aim of the modular coding is to link the entire pipeline in a way so that you should be able to understand how rag actually works and also implement it in your company use cases.

0:47 Let me tell you one very important thing. 90%age of the use cases that are currently been worked in all the companies are specifically related to rag. So this crash course will be an amazing one for you all of you. We'll keep a simple like target of thousand uh try to complete it as soon as possible and we'll also keep a like target to some uh comments target of 500. So please try to complete it and yes go ahead and enjoy this particular crash course.

1:11 Thank you. So this is a simple definition that uh I have put up over here and uh in this definition first of all we'll try to understand rag. Okay. So first of all let's go through the definition and then I will give you a brief idea what exactly rag is all about you know. So here you can clearly see that rag is the process of optimizing the output of a large language model. Okay. So it references an authorative knowledge base outside of his training data set source before get generating a response.

1:59 LLMs are trained on vast volume of data as we all know and use billions of parameters to generally original output for task like question answering, translating and completing sentences. Rag extends the already powerful capabilities of LLM to specific domain or an organizational internal knowledge base all without the need to retrain the model. Okay, it is cost effective approach to improve LLM output. So it's relevant, accurate and useful in various context. So this is just a basic definition.

2:22 You can refer to this particular definition. So guys, now let's go ahead and understand about rag. So let's consider that I have a generative AI application. And as you all know in a generative AI application, usually let's say that I have an LLM. So this is my LLM. Now usually whenever we have a LLM what happens is that let's consider that I have a user a user is asking a query. So this is a my query from the user and before it is sent to the LLM we do add a prompt right we do add a prompt and this prompt is just like an instruction to the LLM like how the LLM should work okay and then based on this we actually get an output now this is a simple generative AI application wherein the LLM is used to generate the content Okay, generate the content.

3:13 So obviously by using this specific technique we give a query and this LLM you know that it has been trained with billions of data okay different kind of data that is available in the internet and based on this it will be able to generate the output. One of the disadvantage of this, let me talk about the disadvantage of this particular approach. As you know that every LLM that is trained, you know, it will be trained for a specific set of data.

4:02 So let's say right now it is 31st August. Okay, 31st August. Let's say this is my LLM model and this is basically GPT5 which is the recent model from OpenAI. Now as you know that when this model was launched this model may be trained by may be trained with data till 1st August. Okay. So this LLM will not have any idea what has basically happened in the current world between 1st to 31st August. Right? And let's say if I go ahead and ask a specific question to the LLM which is between this specific dates for any kind of events the LLM will start hallucinating.

4:52 So one of the major disadvantages of only using the LLM is that it will hallucinate. Okay. When we say hallucinating what does this basically mean? It means that even though it does not have the knowledge what has happened between 1st August to 31st August any events even though we ask any question the LLM will try to generate it own answer because it does not want to look like a fool. Okay, that is the best example. It does not want to look like a fool.

5:16 So it will try to generate some answers and it will make sure that it'll it'll show you answer that you may also have to believe it. that is how it will be written you know in terms of the output that we get so usually this condition is basically called as hallucinating okay so this is one of the major disadvantage the second disadvantage that you have so let's say that I'm using this LLM and you know this LLM has been trained with huge amount of data now what happens is that I'm running a startup let's say now in my startup I'm solving a specific use case and I have some data which again I need to use this particular data along with my LLM.

6:05 Okay. So let's say that I have some other data like you know um policies policies of my company I have HR policies of my company I have finance policies you know and this policies all will not be available in the it will not be available publicly because it is my startup so these all data has been protected now I also want to use this specific data and probably create a chatbot okay now how do I do this Now one way is that many people will say hey kish we can take this particular data and we can fine-tune the model right we can simply fine-tune the model yes this is a very good solution but understand fine-tuning a model is a very expensive process very tedious process because this LLM whichever LLM we are using it has billions of parameter and tweaking this billions of parameter usually takes a lot of time Right?

6:54 So obviously this is a solution but this is a very expensive solution. Okay. Now do we have any other way? Any other way and remember these all policies and these all data will also keep on getting updated as we run the startup. Right? So every time we cannot just go ahead and finetune it like every day we not fine-tune it. Right? So we should try to find out a solution like how do we prevent this? So this can again be prevented with the help of rag right now how it will be prevented with the help of rag I will talk about it okay so here instead of fine-tuning I'm saying that hey I will go ahead and implement the rag now you'll understand only when we understand the pipeline of the rag which I will discuss in this specific video okay now these are the major two disadvantages that you see right over here and yes they are some more disadvantages which we'll just deep