AI product management and LLMs

0:00 My only request is that before you go ahead and start watching this video, keep a notebook and pen with you so that you are able to take notes and have your full attention in the session. I'm sure this is going to help you out and if it helps, go ahead and let me know in the comments. Thank you and happy learning. So, we have divided this two-day master class into three components. The first component is geni fundamentals where we'll talk about the geni value tech types of AIPM and nature of large language models.

0:48 Then we will talk about inside EIPM. We'll talk about context engineering, rag, prompt engineering, finetuning, AI agents and AI events. Even if you're not able to understand anything right now, it is my promise that by the end of tomorrow, you should be able to understand these topics. And then we are going to talk about giving you actual examples how products such as chat, PR, granular, gamma, AI, notebook, LM are actually built. Right? So this is going to be the session agenda for these two days.

1:11 And the motivation or the purpose of this session is not to make you an expert in AI product management because that cannot happen in 4 hours. But the motivation of this session is to make sure that you feel motivated after completing these sessions that yes I have a clear road map. If I can learn this much I can also go ahead and learn and explore the entirety of AI product management. So this is to give you the confidence so that you are able to get started.

1:36 Right? And tomorrow and today and in today's session I'm going to ask you in the end that whether you are feeling feeling motivated after the session or not right now let's talk about the Gen AI revolution. Right? The companies that you are seeing on the screen, they are now among the world's most valuable companies and many of these companies have able to get this kind of amazing transition just after 2022. So from 2022 to 2023, this company Open AI has got a valuation of 500 billion.

2:26 This anthropic company which was not existing few years ago now has a valuation of $183 billion and then we have something called as XAI Elon MRI which has the valuation of $200 billion. Not only the new companies are doing well but the older companies which are betting on AI as a revolution which is Microsoft has gone from so Microsoft was founded in Yes. Microsoft was founded in about 1980s 1983 and from then to 2021 they were able to get a market cap of 1.8 trillion but in the last 3 years they have grown from 18.8 to 3.8 almost twice increase.

3:14 Similarly Google after betting on AI they have grown their valuation almost 2.5x similarly meta has grown too much from 0.3 billion or 300 billion to 1.8 8 trillion and then Nvidia which is the most which is the company which has most benefited from this AI revolution it has gone almost 10x of their valuation right and then we have also seen some very new age companies which are actually created in this period only so ker the company did not existed few years ago is now worth $9 billion glean is worth $7 billion 11 labs is worth $6 billion and perplexity is is worth $15 billion So now there is a sharp change that has happened in the market recently because of which people are able to understand that there is this new technology called Genai which can go ahead and do so many things together.

4:02 Okay, but understand as many people have predicted this can well be a bubble. So how do you check if something is a bubble or not? It might be possible that all of these companies are overvalued in some of the other way. But let me ask you a very honest question. Okay, everyone go ahead and answer me in the chat. When was the last time that you have used chat GP and Gemini? Yeah, I'm sure for many of you your one of the tab is chat GP right now.

4:25 Yes. Cool. Right. So, understand there have been many technologies that have came and gone. For example, there was a uh there was a revolution on blockchain, there was a revolution on cryptocurrency, there was a revolution on NFTs. Although you were listening about all of these things on news on multiple websites and Twitter but they could not become a part of your daily life right so the verdict is that it might be possible that these things that we are talking about the valuation and all these could be larger than life valuation that is completely possible I'm not disagreeing with that but we also have to understand that genai is a technology that has been able to prove itself that it is actually very helpful Because technology if it is only increasing the valuation that might mean that yes it could be a bubble but if it is generally helping you in your daily life then it just means that it is just a matter of time that we are able to find its relevant use cases and utilize it for work and more of our personal life right but how has it happened like what is there in the genai revolution so understand the genai revolution has happened because of a innovation in

6:13 technology which is called as large language model. Okay. So what is a large language model? A large language model? A large language model is a neural network trained on massive amounts of text to learn patterns in language enabling it to understand, generate and reason with natural language by predicting the next more likely most likely word or token in the context. Right? This is the Wikipedia definition of large language models. But what exactly is a large language model?

6:37 Let me go ahead and tell you. Please everyone focus on the session super important part. Yes. So we are talking about large language model. So a large language model is it is kind of a code or an algorithm. It is kind of a code or algorithm or a piece of technology that is very good at predicting the next word in the sequence. That is get very good in predicting the next word of the sequence. Right? For example, I just take this sentence.

7:25 I will take this sentence. I will increase the size. Right? So let's say if I enter this particular thing to a large language model, if I tell a large language model that a large language model is a then it is going to return me neural. It is going to return to me the next word. If I give it a complete sentence that a large language model is a neural then it is going to return me network and then I will keep on iterating so that I'm able to get the complete sentence.

7:53 So what is a large language model? Large language model is the next word or token predictor. And when you do this repeated number of times, you get the complete sentence, the complete book, the complete essay, the complete summary. Right? So this is the basic fundamental how a large model works. You give it some sentence and complete sentences. It will try to complete it. And how this how does it completes it? Let me go ahead and tell you.

8:40 Okay. So understand first of all if you guys are able to observe I have written token in the bracket. So what happens is whenever you give a word to a large language model. Okay. So let's say I give a word to language model that he lives near the river bank. He lives near the river bank. Okay. So large language model is a machine. It's an algorithm. It's a code. it will not be able to understand what are these exact words.

9:26 In order to make sure that the machine is able to understand everything, what we do is we convert this into numbers. So in order to make sure that the machine is able to understand things efficiently, we convert these into numbers. These numbers are called as tokens. Let me show you how it works. So I'll go to a website called as tick tokenizer where if I write this sentence the sentence is he lives near the river bank right now the large language model has converted this the large language model has converted this into these tokens.

10:16 So these are the tokens right? Every model can generate different kind of tokens. Every model has different kind of combination of tokens. Okay. The tokens are generated to make sure that the large language model is able to efficiently utilize its memory and context because numbers are easier to interpret for machines rather than the words. Right? And in total in English language we have around 26 alphabets and we would have so many maybe millions of applications, millions of spellings, right?

10:39 Vocabulary. In GPT4 we have 100k tokens, right? So what is a token? Token is a numerical representation of your text. Token is a numerical representation of your text. Right. And at a high level, three words in English is equal to sorry at a high level in English two words are equal to three tokens at a high level like not this is not completely true in all the cases but for most of the times on an average two words is equal to three tokens.

11:32 Why tokens are important? to understand if you go to any of these application let's say let's understand open AI pricing so if you look at pricing of any of these models you'll be able to understand that they work on tokens how many tokens are input how many tokens are generated how many tokens are cashed right so for every 1 million tokens that you give to charge GP it is going to ask you for the input it is going to take $1.25 $25 from you.

11:55 Right? So what is a token? Token is a conversion of your words into mathematical numbers so that the model is able to understand them very very efficiently. Right? How many tokens are possible in a model is called as the vocabulary size of the model. How many tokens are possible in a model is called as the vocabulary size of the model. Right? So this is this is called as the tokenization. Right? Now what happens the purpose of an LLM is that if you give it a set of tokens let's say 1 2 3 4 5 it will try to predict what is the next token then if you run it again then this becomes your full sentence up to six then it will generate seven and then you have new sentence 1 to 7 and then it will generate eight.

13:09 So it is going to go ahead and complete the sequence. Right? That is at the most fundamental level how large language models work. Right? Everyone a quick yes or no if you're able to understand everything so far. A quick yes or no before I could go ahead. Wonderful. Great. Now let me go ahead. But this is a simple explanation. But how does it happen? How does it predicts? Okay. So what happens is this is how a large language model works.

13:57 Okay. So there are three stages. Whenever you we create whenever we create a large language model, we use these three stages. Every large language model is created by going through these three stages. The first stage is called as a pre-training stage.