Day 3-Live Session- Learning Python With Vibe Coding In 2026

Key Takeaways

Heat. Heat. [music] [music] [music] >> [music] >> All right. Hey everyone, good evening. Good morning to everyone. So we are live and this is day three of learning Python with VIP coding in 2026. Everyone hello guys. Hello. Hello. Hello guys. How many of you are here? Please say hi. Yes, you're here. Hello. Yes. Hello guys. Hello. Hello. Great. Great. Nice to meet you and thanks for joining this uh series where we are learning Python in a new way. Highle Python. Yes, everyone knows. How many of you are following this series from past two lectures? How many of you are following that from past two lectures? Hello. Hey guys, Krisha is also there in our live chat. So, please say hello to Krisha as well. You guys will pro probably see Chris next week and uh I'm not saying anything in advance but Chris is traveling so you guys will see him soon. All right. I just came here. Yes. Uh thanks man. Thanks for joining. Today is my first day. Uh guys, you will enjoy today's days. Okay. I'm new here. I just came. That's great. I can see a lot of people are new here. But uh don't worry at all. I here we are learning very different way uh to connect with Python to connect with coding. All right. Yes. People are saying hello Chris. Yes. Hello guys. is also able to see your messages. First day for me too. This is my first lecture of VIP coding. That's great. That's great everyone. All right. So, is everyone ready? Can I start today's lecture? I don't want to extend this class unnecessary but we're going to learn lot of amazing things in today's lecture. Yes. Great. So, let me do one thing. Uh, let me pull up my screen here and then we'll start. Great. Just wait guys. I have enrolled for data science 2.0 and was going through your Python live class video. Uh, that's great. S go. Uh, probably you will learn a lot from those lectures. Can we purchase this as a course? Guys, uh this live session is already free but you can purchase the data science course which I will talk about after some time. All right, in today's lecture only I will talk about it. So don't worry uh we'll talk about more. Okay, thanks for bringing it in. Some people are saying thanks for bringing uh this series in. You guys can see those chats as well. Okay. All right. Uh so let's get started and uh let me know once you are able to see my screen just wanted your confirmation how many of you are able to see the screen yes hopefully yes great I'm primary interested in w coding do you have a course for this specifically guys uh we'll talk about lot more about w coding uh this whole series is kind of targeting it but we cannot always target just the wide coding it is totally different. All right. So see uh in this class this is the day three of this particular series uh what I'm going to do in this uh lecture particularly today that is day three I'm going to recap what we have done in day one and day two and here we are going to talk about how to actually uh use LLMs using Python without even knowing Python because that is the thing which I told you in day two that we should not rely on a coding language but the coding fundamental itself Here we are not learning Python from the very scratch. You guys can check research Python playlist to understand everything in more depth. But this one is more specifically about uh this is a piece of code which you can ask chat GPT, Google Gemini or cloud whatever to your liking and then how to interpret is is what we are focusing on. But also in this particular class we are going to have a very small mini project. We are not going to do anything fancy but you guys will see the power of lls of okay just give me a second my pen dropped. All right, I have it. And also because uh to talk to an LLM, we require a a specific kind of a language like they don't require a specific language, but if you follow a certain kind of a way to communicate with LLM, you will get the best possible answer from that LLM. So I also want to introduce you that as well. All right. Uh okay. All right. Yes guys, uh this session one and two you can check with the same name on the YouTube. You can see uh learn Python with wipe coding in Chris's channel to find day one and day two. Don't worry, we are every we are covering uh everything from the very different perspective. Okay. So let's talk about the recap then we will jump to the next part. So everything is present in this particular node. So it is also easy for us to understand what we have done in day one and day two because day three there are a lot of people who are joining. So in day one specifically we have understood that uh what exactly this whole ecosystem of AI now in 2026 I know this is 2025 but now we're targeting 2026 this is already the end of 2025 right so if I talk about the whole ecosystem we have more than thousand topics to cover to be able to get to the latest tech that we have today the latest tech that we have today is agentic AI right but to even understand agentic AI in very very very depth. We need to cover more than thousand topics to understand everything. But uh for someone who is just getting started into this field, uh it is very understandable that uh someone will get overwhelmed from all of this topic. And if you follow the traditional way uh to learn something, it might take uh uh one year, two years, one year of learning, one year of practice. And we don't have that much of time because within 2 years the new technologies are going to come. Then we have to again uh learn from the learn from where we have ended the previous learning right. So we need a better way. So what we told about that we should not specifically target uh the programming but we should target what the business logic we are solving because in 25 LMS are able to do your coding. So we require someone uh because in future LLMs are only going to get stronger. So what we require is a mindset of a developer, a mindset of a architect who can use LLM to their advantages to solve the problem. So the target is not going to be the code. The target is going to be the solution solution by any means. And here the means is going to be white coding using LL white coding using AI. So we are moving from coding methodology to solving the business logic and this is how someone who wants to excel in the future needs to target that is business logic not the coding part. All right. So in 2026 you can uh what we have seen that uh there are multiple ways to install Python and in the day one we have seen that uh out of those ways we have chosen the faster and the latest method that is via UV we have installed Python in our system and we have also understood the advantages of UV. Then we have also seen that uh something called as project environment. How to create those project environment. Why exactly we need project environment and how do we even create those? Then we talked about uh venv which is used to create environment and help us manage this different environment. This is something which we have covered and don't worry if you are someone who starting today just watch this lecture be present in this class. you will be able to understand at least the flow of what we can achieve with VIP coding what we can achieve with using LLMs. All right, you don't need to watch the day one and day two today. You can watch it later but for now it is not in requirement. Just look at the screen what I'm doing. If you're not able to understand something, forget about it. Just look at the next one. All right, that's the whole vibe. So using UV we created Python and using UV we created the virtual environment. Then we create multiple projects. These are some commands which we have used before. Then we talked about the difference between uh editor uh as well as ID as well as agentic ids that are present today. Uh few months back this agentic ad ID is are coming into the picture which has both editor plus coding and whoever is not able to understand all of these terminologies don't worry about it that is the main goal of this whole series. You can watch it later as well. All right, this is what we have done and those kind of ideas one of those example is Google anti-gravity. All right, so uh guys Chris will come uh next week. Chris is traveling and we'll be back soon. All right. Uh so in the day two what we have done in the day two we have installed the VS code and uh did some installation on the extension. So it will help us do the coding. We understood what is a terminal. We understood what is a notebook versus the script. We have seen Google collab. If our system is not capable, we can use that. We have seen little bit of Python and we have seen how to call an LLM which I'm going to introduce today as well in more clarity. But in terms of Python uh because this is a series which we are not going to uh cover the whole Python in detail. Want to go and learn the actual coding you guys can learn from Chris's YouTube playlist or you guys can join our courses which I will talk about. But Python from my point of view because this series is going to be short. I wanted to give you clarity in terms of uh what are the different logical separation in coding. Then I talked about what are those? I talked about how what we do with the terminal. I talked about in 2026 what we do and Python statement. How do we approach to the solution? We create different task of it. Then we understood from the real world analogy that is house building. We understood that how can we compare variables to a real world understanding what actually variables are. What actually functions are functions are nothing but some kind of action. What actually loop does right and what uh uh uh decision making how can we make a decision making using programming. So we have seen that as well again on an highle overview. We have seen that whether we can combine multiple blocks to create a larger proc. Yes, that was a possible that we call it as a bigger function or just a traditional function which has more task involved in it. We have seen build wall build example. We have also seen the entire building and understood that two concepts of blueprint versus creating the actual object from it. So now we understand that as well. Once that is done, we have also seen uh we have done a very simple exercise where based on the problem statement uh we try to think programmatically that how and what exactly we require to complete that without even knowing any of those Python. All right. So we have done that as well very simple experiment and then what we did and this is day three where we are going to start the new part. All right. So is everyone at least have an understanding highle overview of what we have done previously and what we are going to do now. All right. Okay. So first let me target where exactly we sit now and what to expect from all of this. All right. So today uh this is where we are. Let me first create this whole line on an highle overview to work with uh uh any LLM or to even start coding we require a language. So that language we are using it as Python. Python is the language we are using and using Python our main goal at the end is nothing but how can we learn AI right? So this particular series on an highle overview is targeting only these two sections. Okay, it is targeting only these two sections and once you guys are more familiar with the highle stuff that will help you understand the overall ecosystem then you guys can go into the level two. So this one I will call it as L1 that means level one. So now that if you want to learn more about this one things does not end here. This is where it is just starting. So now let me write L2. What exactly this L2 is? In L2 we are again going to start with Python. We are again our final goal is AI but now the details are going to be increased. Once the Python is done we also need to learn machine learning. And to learn machine learning we need to learn statistics right and then we can jump to the first level of the L2 level of learning AI. So let me write that as well. I'm just giving you a road map everyone because uh this is not a series where we are going to learn the coding but where we understand what exactly we need to target in ro26 by learning the highle things. All right. So is everyone clear with the level two python statistics machine learning you can achieve the base level of AI. This is a AI which uh you cannot solve using LM and generative AI. Is this part clear to everyone? Level one is what we are learning. Level two is what should be your next target. Then if you guys are able to understand then I will talk about level three. Is everyone able to understand this? Guys I'm looking at the comments now. Say yes, say no. Just giving you a road map. Yes. All right. So now I will talk about the next depth where we are again going to have this is level three. Again we will start with Python. Our end goal is still AI but in between we have machine learning. We also need to learn deep learning and within deep learning we have multiple parts. Okay. Within deep learning we have computer vision plus NLP. This is the specialization of deep learning. Then in between what we have is this is what we need to learn. Okay. Uh let me write as generative AI. [clears throat] Someone's hacked Chris. No guys. Uh no one hacked Chris account. All right. If someone will hack Chris account then why will they teach AI again in Chris's account right? Yeah. All right. So now that we have uh cleared the level two now it's time to go to the deep learning part of it and you can choose any of the specialization whether you want uh computer vision or you want language processing but you need to learn the generative AI. All right. And once generative AI part of it is done, now comes the very very very final level which everyone should target in 2026. All right, I'm giving you multiple levels. L4 again we will start with Python and everyone can guess now what we are trying to do. Uh here I forgot the stats. So Python we will target stats. We want we need to learn machine learning. We need to learn deep learning, computer vision plus NLP. Then we need to learn generative AI. Then what we need to learn? We need to learn rag. We need to learn rag. We need to learn agentic AI. Then only we can say that we understand end to end of 2026 AI. So now you can see the complexity keeps on increasing. But if you learn with this multiple level approach then only you will be able to succeed otherwise you will get overwhelmed. So currently we are at level one we are seeing the high level of it. So at least once this series is done we know that okay uh maybe I'm interested in one of these technologies I will start learning that I will go to the level two then go to the level three go to the level four. All right. So this is what we will be targeting. So we are no way near to the end of any of this AI final thing. But if you guys want to learn that. All right, let me just give you uh I want to show you something. Most of you already know that and whoever is attending this two days. All right. Uh 30 more seconds I need. All right, got it. Yeah, I'm on one confusing question. The batch to is it very specific for data science? All right. Uh see I'm going to talk about it. Okay. So guys uh say this uh this is krishna.in uh this is our favorite mentor Krish and here uh if you go to krishnak.in if you go to the live classes uh one specific batch which is going to target this is batch number 2.0 version 1.0 batch is already ongoing but this version 2.0 to batch is very very very special because it does not just target level two and level three but it also targets till level four. So within a single whole course you're going to have all of this uh uh levels all of the all four levels included into a single thing. So you don't have to reach to anywhere else. So if you check this particular thing uh yes so you can see this is ultimate data science and geni boot camp. If you want to learn more about it, you can click on just learn more and then you guys will be able to see the syllabus as well. If you guys want to raise a query, just uh send your name, email address and send a message. Team will connect with you. And if you uh want to learn everything of the data science and AI, you can just click on enroll and you guys can proceed but you guys can also check the syllabus here which is what I am interested to show here. So if I click on the syllabus, thank you for clarity. I believe I am in the right place. Yes guys, everyone is in right place trust me. So this is the whole syllabus. You can see this is version 2.08. This looks very fancy and here you can see Python stats, machine learning, deep learning, jai, rag, agentic. These are all the things which I mentioned here. I hope everyone can see the same thing. But don't think that we are only going to teach things in very high level. If you see here, this is just the start and it is meant for people who is from non- tech or tech and no prior experience is required. This is going to be 10 to 12 months course because within 2026 we uh you guys are supposed to learn everything. So you guys can ready for before 12 months you guys will be ready to apply for multiple uh job descriptions or as well. This is a required skill is beginner as I mentioned no prior experience is required and if you this is Christian academy team uh this is the team which will teach you everything. We do have lot of uh different offerings available if you guys will check it we offer everything from the specialization all the way from uh the ultimate data science geni boot camp as well. You guys can check everything. All right. So uh uh this is module one. Here we are learning Python foundations. So see this what Python I'm teaching you I'm teaching of high level because we will not be able to complete it within live course within this uh live YouTube session because it will this Python alone will take you one month of time we don't have one month of time here to learn till the LM part right then we have advanced Python then we have numpy and everything is in very very very detailed you can see this is data analysis with pandas we are going to do data analysis data visualization we are going to learn database also we are going to build applications using using streaml. We are going to learn the statistics from the very base to the very advanced part of it as well. We are going to learn the machine learning. We are going to do some ADA. If you lot of you will not know ADA for now but later once you understand all of this it will very easy machine learning you are going to learn but all the way if you check now here at the end we have total of 27 modules in this course. So this is not a short course and the final thing is multi-agent system for research automation you guys will be building. All right. So this is krishnack.in you guys can check it and uh here you will be able to find this ultimate data science and once you join in you guys will be able to see everything your community course there are a lot of people who have already joined. You can see all of these messages here. This community is already created for ultimate data science people and just like that we have all of this uh courses running in parallel and here none of those courses are for just it we will teach you beginner. No our main target is even if you're beginner we will teach you from beginner to the very advanced and you guys will be able to see that uh uh you will have a separate community you will have a separate dashboard workshop courses courses recording will be available and whoever joined this particular batch 2.0 to live session they will also get the version voint version 1.0 zero recording uh uh available to them. All right. So you guys can if you want to do the fast track you guys will be able to do it. So guys trust me this is the most detailed as well as uh uh what I will say the quality learning you will be able to get anywhere. I trust means anywhere means anywhere. Okay. You guys can check also the feedback which you can check on Chris's LinkedIn account as well. So this is about uh level four. So whoever is interested for level four, they should head here. All right. Is everyone clear about this? Level four is not the target of this series. Level four is something which we all wish to add. We all wish to learn. All right? And this is what you guys should follow. So now I will talk about uh uh the next part. How many of you know what actually is a LLM? How many of you know what actually is LLM? Tell me what is an LLM? Anyone of you knows what is an LLM. Tell me. Waiting for your answer. In the meantime, I just need LM. Where can we get session one and two guys? All the sessions and everything is available. Okay. uh LLM is a large language model. Yes, that is the full form. But uh I will give you more clarity because today we are going to jump into that. So let me write LLM. Okay, before LLM tell me in two three words what is a Python? Tell me two three words what exactly is Python? What is a Python? what we should uh how much we should worry about Python in 2026. Yes, we should worry about it for now. We should consider Python as a programming language. Correct? We should consider Python as a programming language. All right? Programming language. And if I talk about LLM, the full form is large language models. Large language model is a subset of you can see generative AI. So large language model derives from generative AI. Okay. In generative AI part of it we create model which are very very very large in nature. And if you don't know what is a model of course a lot of not know what is a model. A model is nothing but if I talk about model a model is nothing but a mathematical uh you can say language. No it is not a mathematical language. Python is easy to use. High level programming language. Yes, we can say a mathematical model of knowledge. I'm talking about in terms of very simple language. Let's suppose knowledge means you know all the physics. Physics is our knowledge base. If I convert all the physics into an understanding which computers understand that is convert things into a number into an understanding convert physi convert physics okay physics I'm talking about in terms of subject physics science all right if I want to convert physics into a numerical way which our computer understands that we will call it as a mathematical model of physics is everyone clear guys it is not a black box we should not consider as a black box. H how and where it is stored is a black box. But is everyone clear what is a mathematical model? Mathematical model of a physics means physics that is converted to numbers because computer understand numbers and those numbers is computers are able to understand from those numbers. So those numbers are not just random. They do mean something which computers can understand. Is everyone clear? What is a mathematical model of knowledge? Guys, are we clear? Clear? Tell me yes or no. Is everyone sleeping? Yeah. Okay, great. Someone said yes. So, I'm moving forward. So, here we can see there is one more thing attached to it a methodical model that is large language. So these are special models which are large in nature which is of huge in size and language it means it understands language as well. So these are big models which understands language and language means not just I'm not talking about uh the native language which you speak or the language of a particular country. No language here means whatever humans speak. I'm talking about in terms of humans speak. So this also includes any knowledge any knowledge that is any knowledge that is written and is present in a text. So text is nothing but a form of language. It can be English, it can be Hindi, it can be French, it can be anything, right? But if I have all the text in the world, if I collect, if I collect all the data, all the text data, all the text data from the internet is very very very huge. That whole data will itself will become very very very big. Big big I'm saying big three times. So if my model my mathematical model has a understanding of a very very very large data or a data set those kind of models we call it as large language model. Is everyone clear what exactly is LLM? a mathematical model of a very very very large data that itself is called as uh LLM. So if we are able to create a mathematical model of a very large data set it means it means now I'm going to write what exactly LLM do now we are able to create LLMs what now what exactly we will do with those where it is used well LLMs because they are they have vast amount of data in it they can read your instructions if If I talk about your calculator, does your calculator has a ability to understand you? Does your calculator has an ability to understand you? How many of you think that your calculator can understand you? What you are saying, what you want to do. No, calculator has a task calculation to the calculator and calculator provides you with the response whatever is the output. Calculator is intelligent. No, calculator cannot read your instructions. But calculator is also a code calculator we also build from coding. So this coding is separate from the coding which we use to create large language model. This maths is different from the maths present in calculation. All right. So to learn all of that we need to go all the way till level three level four and somewhat level two as well. Level two is where it all started. All right. So LLMs can read your instruction. If you pass something in Gemini that I I want to write a leave application does uh chat GPT and Gemini is able to understand your instruction. Yes, they read your instruction and they provide the output what you want. It means they also have a capability to generate text as well as content. Content. Hello. Hi from Iran. Someone said hello thanks for joining. Next lm can read your instruction. LM can generate text contain. Now it can also generate image as well as videos. This is I'm talking about in 2025. LLM can write or or and let's say LLM can write and improve your code as well. This is where we are interested in. We as a developer we want a companion which can write us code. All we will do is provide with the best in instruction possible the code which LLM provides. We are going to debug it. We are going to fix it. We are going to run it. But we will not waste time two weeks, one month, 3 months to just to create a base code. We can ask our this mathematical models to create the base code for us so that we can start applying our knowledge on top of it. This is how we are going to use it and run 26. But here what we're talking about what LLMs can do. LLMs can follow rules as well. LLMs can follow rules as well. LLM can convert formulas. LLM can translate. LLM can fix grammar. LLM can analyze your data as well. So what we got by creating a mathematical model from all the data in the world, we got a very very intelligent system which can do all of this. Is everyone able to understand LLM guys? Is everyone able to understand LLMs? Great. Great. Is everyone enjoying today's session? Are you learning something guys? Are we learning? Great. So we as a developer, we as a developer, we as a developer are going to use Python are going to use our Python skill plus LLM's knowledge to do 10x faster development. 10x faster development. Okay. So even beginners, even beginners uh even beginners are capable as seniors. Now but with guidance you cannot surpass a seniors until unless you have that knowledge. But in terms of writing code and somewhat more guidance even uh someone who is starting as a intern or someone who's starting as a fresher can now uh write code as a senior person. All right. So what we need to do what is our target? What should be our target? As I mentioned, our target is to think like an engineer. To think like an engineer, not a coder. So all the coders who thinks like a coder I are going to be replaced in future. But all the coders who thinks like an engineer is going to retain their position in future. So is everyone clear? What do we mean by Python plus LLM? Is everyone clear? What do we mean by Python plus LLM? So, whatever things we are able to complete, I'm going to mark it. Okay. All right. Done guys. RG is LLM also has some disadvantages and those disadvantages we can solve with rag. But today we are not going to system. Hello guys. Hello. Hello. All right. So now that we are done with that, now I want to talk about uh prompt engineering. Okay. Now tell me uh suppose you're asking me a doubt. Tell me which one is better. Which one is better to ask me? All right. Sir, I have a doubt. Second question is sir I have a doubt in LLM and how exactly we can create one. Which one do you think is better to ask? Which one do you think is vague? Which one do you think is detailed? one or two tell me everyone if you guys are also uh uh in interact in the chat it it will also help me to teach you better all right which one is better one or two tell me why because two contains more details so similarly prompt engineering is a way to ask llm in a way that we can get the maximum advantage from okay you can get maximum advantage from all right so that is prompt engineering a way to communicate with the LLM as a developer to get the best possible results from it so instead of asking some kind of a vague question instead of asking some kind of a vague question we will use the full potential of LLM using techniques present here and then using techniques using those uh sim similar instructions we are going to create our small mini project today which is not going to take it yes because it contains more details all right because it contains more details so the better the better the it's not always about detail uh suppose I have 200 lines of detail there are some uh disadvantage of lls which uh you will understand once you know how to create them how to read them but for now our task is how to communicate with lls better okay so what we need to do be our target is going to be be specific. Okay, be specific. One thing before prompt engineering is that I wanted to give you uh a mindset of a developer when we talk about leave application. when we ask leave application to Google Gemini to Google Gemini [clears throat] as well as chat GPT there are others but I'm just giving you this example the output it provides the leave application the leave application that we got from all of this do we need to change something or it comes out to be perfect Can you copy? Can you copy and paste? Can you copy and paste as it is or you need to make some changes? Can you copy and paste or you need to make some changes in the final output of that leave application? Tell me we need to change few things present here. So when I talk about a application, when I talk about a calculator, when I talk about a calculator, I write 2 + 2 as a instruction. Does calculator outputs the final thing which I can copy and paste or does it output something like hey this is your answer. This is your answer or answer option one or two. Which one is more frustrating if you get an output? One or two? Which one is more frustrating for a task of a calculator? Tell me which output will be more frustrating if we see that as a output from calculator. Yes, correct one. Because what we expect from a calculator is the final answer. We don't want any unnecessary information there. We just want to do 2 + 2 and then what we get is a answer. All right? We we want option two as the answer. So that's what happen in programming as well. In programming, in programming suppose we are creating a we are creating a task. Okay, we are creating a task. We want to create a task and if you guys don't know in day two I I just want to show you that you guys can revise the same thing later. Okay, in day two I talked about this action. Okay, I talked about this action. You can say it is a action or task. So that particular thing I'm talking about. Let's suppose using programming you want to create some task or some both of them are correct. some task or some action you want to create using your program. In programming, you provide a specific input. It should be a specific input and you also expect a specific output. All right? You also expect a specific output. And using LLM, if you use a LLM, LLM does not give you a specific output. It gives you something like this. So let me show you uh let's check an example if it is present here then I will show you the same thing. Just give me a second. Okay. Let me load that. All right. I think I have it here. Okay. I hope everyone is able to see my Google Gemini. Yes. So, see this what I'm trying to do here. I will ask and let me zoom in. I will ask an LLM do 2 + 130. Okay. And tell me the answer. You can see it is giving us this unnecessary thing. But the output is 132 which is correct. But here as I mentioned in programming we want to get a output which is suitable for other system. I want to create system by system. S system one the output of system one will go to system two. Output of system two will go to system three. Output of system three will go to system four. And to create such kind of a system I need exact output instead of what humans read and write. We want a programmatical way. So if I talk about 2 + 2 the answer should be four. If I talk about uh let's say what is the weather today? What is the weather today? It should either give me uh it is sunny very very specific answer. So only when we have a very specific answer then only we can pass this answer to other system in programming. You will understand why this particular method will not work later but for now all you need to understand is we need a specific specific answer. Is everyone clear? Is everyone clear? And for people who have attended day two, we want an answer. We want our input to be stored in a variable and we also want our output to be compatible to be stored in a variable. If we can create if we can create that specific answer then we can create better systems from it. All right. Better systems from it. All right. So using prompt engineering using prompt engineering see this uh where is our solution? Yeah. Now see this what I'm going to do? I'm going to provide a better instruction. Uh you are an calculator. You are a calculator and your task is to just provide only the answer and nothing else. Now I will say do 2 + 130. Are we getting the correct answer now? Are we getting the answer like a calculator? Tell me. Yes. No. For people who have watched day two, will we be able to store 132 into a variable? As I told you, in day two, I told you that variables can be stored. That variables are of different types. Some store text, some stores numbers. Let's see where are those. Yeah, I told you that variables, some of them store text, some of them store numbers. These are numbers. So if I do a calculation, I want it to be stored in a text. Uh if I doing calculation, I want it to be stored in a number. If I'm doing some kind of a paragraph or something, I want it to be stored in a string. So that is a basic foundation which you need to get clarity later. But we want a better way to communicate with the with the LLM chat. This Google Gemini is nothing but LLM and that's what we are going to learn now. All right, that is prompt engineering. So just I'm going to do the basic prompt engineering. I can teach you for four or five hours as well but we don't have four or five hours. All right. So we are just going to learn how can you watch missed classes guys. Uh you guys can check Chris's uh playlist and there you will find all of this video day 1, day 2, day three and the series name is Python with W coding in 26. All right. So let's learn the prompt engineering here. Okay. Guidelines. I'm just giving you guidelines so you can communicate better just like I did. Prompt engineering guidelines and then we will do a very mini project. I've already created the project. You guys don't need to understand the coding. I will help you understand all of that and then we will see a awesome thing at the end. All right. So give me a second. Guys, are you learning? Are you enjoying today's session? How many of you are learning? Even without knowing a single piece of code, at least if you're able to understand 50% of the class, it is more than enough, guys. Okay, it is more than enough. All right, see this. Now let me write uh techniques techniques of prompt engineering. So there are on an high level there are few techniques. I'm going to only mention three of those. All right. First three first technique is called as zero short prompting. Okay, prompt is nothing but a instruction which we are sending to LLM. So we already know what is prompt. We will learn what is zero shot. Don't worry the name might be scary but it is very very very simple just a technical name. Then the next one is one shot not one hot it is one short prompting. And the next one is few short prompting. Okay. Few short prompting. All right. And these two techniques uh within prompting also within each prompting we have different methods. But what we are going to focus on let me write that as well. Okay. Now I will talk about prompting then we'll explain each of them. Don't worry let's pick this color. This looks fine. Okay. So prompting itself that is the way you ask llm are of two different parts. One is called as structured prompts which we are not going to touch in this class. Okay. What we are interested is in unstructured unstructured prompts. So see this prompt itself has two different types and each of these types have different techniques by which we write prompt and don't worry it is not going to get complicated. So what we are focusing on is this one. This is what we are going to do. So this is nothing but let me write this is nothing but your natural natural preform. Okay, let me write it clearly because natural free form conversation conversation input all right conversation input now let's get to what exactly all of this guys uh coot one hot one short prompt uh few short prompting zero short prompting I'm talking about the high level guys. We don't need we are not going depth in any of these places. Now tell me does this feel like a natural uh free from prompting? Do you feel like as human beings this is natural to us? This is our natural way to communicate. Yes. This particular way of prompting itself is called as unstructured prompt. All right. This is what we are going to focus on. So our focus is already on one's uh unstructured prompt. Are we clear? Are we clear? So let me write unstructured prompt. I'm just going to copy and paste the same thing here because this is what we do. There is no structure to it. This is how we are used to how we are used to communicate. All right. So yes, this is the example of that example. All right. So this is what we're going to focus. Now I will talk about these three different techniques and one by one I will cover it. It will not take long. Zero shot very simple. Zero shot and yeah in zero short we provide we provide direct instruction direct instruction or question that's all we only provide direct instruction or question so tell me this prompt is bought how many of you are able to guess it here We only provided the direct instruction or question. That's all. This is our instruction. This is our question. You can either have instruction plus question or just instruction or just question. But the direct way is nothing but your zero shot. So what are exactly the other parts? The other parts if I talk about if I talk about one shot in one shot we provide one example one single example of the desired output format desired output format. So here in this particular prompt, if I write something like this, okay, this is the prompt which we did originally. But if I copy and paste this prompt and if I talk about examples, if I write some examples here, example one. If I do 2 + 2. All right, output I want is four. Now if I do 2 + 200 the output I want is uh 202. All right. So if I only provide one example this I will call it as one short prompt technique. Are we clear? Let's do this and see the answer. Is it working? It is are helping the LLM what exactly the input is going to be and how we are expecting the output from the LLM. So we are helping LLM to provide us the output in which we want. All right. Similarly, if I provide multiple examples, if I provide multiple examples in this particular scenario, LLM is very intelligent to do all of this. So it is not facing any issue. We don't require more examples. But if we provide more examples, this one we'll call it as few short prompting. Here we help LLM with more examples. So the more complex the task become. If you provide more examples, LLM will be able to understand it better. This time the task is very very very simple for an LLM. But when the task is uh very complex then you need to provide as much example as possible to help LLM. So is everyone clear what is one short what is few short. If your output from LLM is not working perfectly try to have as more example as possible. So here we provide here we provide small number a small number I will write typically two to five of example okay 2 to five of example to demonstrate to demonstrate the desired pattern Are we clear? Very simple, guys. Is this simple? Now, I'm going to give you a shortcut to a blueprint to write a better prompt. Is everyone clear? Now, I'm going to give you blueprint which you guys can use uh anywhere. Anywhere you guys can reuse. Okay. So prompt blueprint just like we all know a blueprint of uh uh leave application we should have our name at the top then the description of it whom we are sending to and then close that application we have a blueprint of it so now I'm going to give you a pro prompt blueprint for developers otherwise if you're not a developer you are not working on a very complex use case you are fine with this kind of a technique. Okay. But if you want better results, you want to give you few things here. Okay. So for prompt blueprint, you guys need to provide role to uh your LLM. You need to provide a role to your LLM that you are a calculator, you are a chef, you are a data analyst, you are a data scientist, you are a creator. You need to provide role to your LLM. with task that you are a chef is to do x y z thing that is the first thing that you need to provide in the prompt to and to help llm behave in a certain way. All right. Then you will provide the domain. Okay. And we will use this domain to narrow down our to narrow down on our task specifically that is you are a uh you are a chef and your tasks to create such dish and let's say for particularly for French dish. French, it's not a domain, but you're targeting or asking it to be very specific. And the more specific you can be, the better your answers are going to be. And then you will define that what is your goal or the final task and here you will write it more clearly. Don't worry, I'm going to give you that paragraph kind of a thing. All right. Mention that what kind of input LLM is expecting. Is everyone clear? What kind of input you are going to pass to the LLM? All right. And then you can pass few examples based on zero shot, one shot, few shot, whatever you want to provide, you will do the example. Also, you can if there are certain categories you don't want to target but your LM targets it, then you can also mention something like exclude. Exclude you will write which of this X Y Z things you want to exclude so that your LM will not target. This is something called as optional. I know it is becoming a little complex but once you will see a prompt you will see the output you will understand that wow LMS can do all of this right. Once everything is done then you will ask LLM how you want output. You as a Python developer either you want output in a integer, you want output in a float, you want output in a text and because I have not taught you Python, uh in Python we have more data types like list, like dictionary, like tupal, these are more advanced advanced type of variables. containers. These are basic types which I've covered in day two. Basic type of variable. And what is a variable? Variable is nothing but a container to hold something. If you want to hold more complex things, we will use list, dictionary or tupil. So in the output, we want to specifically mention to LLM that we want our item, we want our output to be a Python tupil. We want our output to be a Python number. We want our output to be a Python string. We want to specifically mention that so we can use it in other system. Is everyone clear with the very highlevel blueprint? Yes. Yes. In this sequence, you need to follow all of this. All right. I will show you example and from that example, don't worry. It is very awesome example. That is what we are going to build. So now we have also covered the prompt technique on a very high level overview. All right. So let me mark that as green and then we are jumping to the final part of today's lecture using uh we are going to use LLM uh in a mini project and then we are going to see some awesome results from it. All right awesome results. Okay, first let me talk about the problem statement then we will solve it. It is very less line of code because LLMs exist. All right, because LLMs exist. So what where we are? We are in mini project. We are in mini project. Okay, I'm giving you problem statement. Everything starts with a problem statement. I'm giving you problem statement. Let's suppose uh uh we talking about kitchen. This problem statement is related to kitchen and cooking. Okay. Problem statement is from cooking domain. And here let's suppose you want to cook a Indian dish. You want to cook a Indian dish and uh uh specifically what you also want is you don't have all the ingredients and you don't want to buy any. So you have X Y Z ingredients. You want to cook a Indian dish but now your brain is not working. You don't want to think about what dish I can make or maybe there are a lot of dish which you don't know how to cook and how you can cook with only three ingredients. So what you will ask? You will send this information to an LLM to an LLM and you will ask LLM a structured output just like I mentioned here. You will ask LLM how much time it is going to take the take to cook this dish. What is the dish name? What are the ingredients in and in the quantity ingredients you already mentioned? And what is the instruction to cook it? What is the instruction to cook it? Only from a single prompt only from a single prompt and the output which you will see will be very very very structured. So our problem statement is because lot of the time we get frustrated we don't know uh what dish to cook it it with any of the uh any of the different different cooking styles right if I want to use French cooking method if I want to use uh different any anything and based on the ingredients it will provide us all of those things is it useful guys is this useful do you think this is useful for us yes this is very very very useful and Because we have LLMs available to us. If LLM does not exist, we need to write If LLM does not exist in alternative, we need to write thousands line of code along with database, along with machine learning, along with Python to create a solution which can be very very very basic and can tackle some of those cases but not all. But LLM can tackle all the cases exist in the world. All right? At least I'm talking about here. So this is the alternate way which you guys can target. That's why a lot of the companies now want to use LLM because LLM reduces. Let's suppose this one is going to take 3 months of our time. This this one is going to take 3 months of our time to create a solution for this one. But once we have LLM, this is going to take us one or two hour of the development to create a solution. Which one is better? Which one is better? Tell me which one do you think is better? Option number one, option number one or option number two? Tell me which one is better. one using LLM. Okay. So, LLM also introduces uh reduces the complexity from a lot of things and that's what we are going to do. All right. That's what we are going to do. So, let's start using it. And in the previous class, I showed you one thing that is let me show let me uh bring that up here. that is I am using Google AI API key and we'll talk about the code what we are doing and then you guys will be able to understand it okay so here this is uh my aistudio.google.com google.com you guys can sign up here and you will see this particular page okay these are the codes that are available already here I'm using the same code but uh the way we are writing the prompt is very very very different and very standard and what we need is from this particular page we want this API key present here see this I'm again showing you this is the main page here we can see view API keys if you click on here we just need to copy the API key you all will be able to see my API key but after today's class I'm going to delete uh my key because if others will use it unnecessary I I'm going to have errors all right so don't try to use my API key which you're going to see I'm not going to have it in a secret key but otherwise people will not be able to understand it all right is everyone ready guys within uh less than 20 lines of code we will be able to complete it only because of using LLM are we clear can Can I start the class now? The final part. The final part. Great. All right. So, I'm going to copy this API key and then minimize. Then we will go to our VS code. I already told you how to create Venv. Uh we have done this in day two. How to install UV, how to install Python. I have done