🔥 Prompt Engineering Complete Tutorial in Telugu | LLMs, Real-Time Examples | By @tech.boy.deepak

0:00 Hi guys, welcome back to the AI learning community. Today in this video, we are going to give a clear, clear cut, detailed explanation about prompt engineering, what exactly prompt engineering is, and before we start this video, we say that you should watch the video until the end . Because if you only see half of it, you will miss the guide. Plan to watch the video until the end . And the next thing is that this video can be watched by anyone who doesn't know about prompt engineering or those who know the basics of prompt engineering . I would like to explain clearly with good examples and welcome to the AI ​​learning community. So if you are watching my channel for the first time, please subscribe and if you are already a subscriber, please leave a comment asking how this course is and how this YouTube video came about. That comment will boost our video and help us get more subscribers and more views . So please like it,

0:47 tell us in the comments, and if any of your friends want to learn print engineering, share this video with them . Let's drive to the video and I want to give you an agenda in advance of what will be covered in this video. Before that, I would like to reiterate what I informed you all in the last video, but please maintain a short note for all of you. And in the last video, I will tell you who the winners are in the middle of this video and I will select a winner again in this video. So what is that task? Before this video ends, whoever takes complete notes of the class I mentioned and posts it on LinkedIn with a good post and a good description and mentions me, I will select a lucky winner and send you rewards on behalf of Tech Boy Deepak. So what am I going to explain in this video? First, what is print engineering? What is print engineering? Second, why should we

1:32 learn print engineering? What are the benefits of learning it? Third, you all know how LLMs work. Large language models, how do they work? Fourth, I will explain the structure of a Prompt: When we ask the chargepity for a prompt, in what structure we give the prompt, it will give us good outputs. I am going to explain this. And the fifth thing is, coming to the final point, what are the techniques involved in prompt engineering? If you open the six powerful techniques that I have brought, you will also become a prompt engineer . So without further ado, let's dive into the video and don't forget to like it before it starts. Your like and comment will definitely help boost our video a lot . So first, as we thought, let

2:17 's discuss what prompt engineering actually is. I expect you all to take notes and write down everything I say clearly because if you write this down now, you'll understand that next time you need to be prepared, just by looking at a basic brief . Okay, so if I create a document, I 'll explain from this. First, let's talk about the basics of what print engineering actually is. Before we start, let's talk about it. Basically, when we are talking to a person, if we don't convey it properly, they won't understand. The same goes for a mission, if we don't ask the requirements of what we want properly, it won't make sense. Right, so let me give you an example. For example, you join a company as an intern and the founder says, "Okay, just in case, I'm hungry, should I go get a burger?" You

3:03 go to the nearby McDonald's and think, "Let's go get a burger." So you go there. You ask, "Veg, non-veg, what should you get?" You don't understand whether your founder prefers veg or non-veg. You come back and ask, "Veg, non-veg?" He says, " Non-veg." You go again. You don't understand whether to add sauce or not when you go back. You go back and come back. So before this, if your founder clearly tells you that I need a non-veg burger without sauce, you will go and bring it at once . So what happens here is what happens to your times. The clear prompt he explained is that you are also bringing him the requirement output . So, similarly, if we explain properly what we want from a mission, it will give us outputs in return . Let's look at that in the traditional way. Prompt

3:49 Engineering is the art and science of communicating with AI models like ChargePT Claude or Gemini. This is how we communicate with AI models like ChargePT Claude Gemini. What is prompt engineering? Let's first look at what prompt actually means in prompt engineering. How to say prompt, that is, a sentence. We open the AI ​​ChargePT and give it an input, write a message, so that sentence is called a prompt. What is engineering? In my perspective, I say crafting. So how do we craft an input in a way that a chargeee can understand? This is prompt engineering. So this is that. A professional example. Whether you want to write it in an exam or you want to say it in an interview anywhere, prompt engineering is the

4:35 art and science of communicating with AI models like chart, GPD, Claude or Gemini by crafting clear structured instructions. Remember this. Structured instructions are very important. We call those structured instructions prompts and that guide the model to give accurate, relevant and useful outputs. Depending on the prompt we give, that chargeee or Gemini AI models give us a clear output. So let's look at an example. Think of it like this. Imagine AI is a super intelligent assistant who knows a lot but has no common sense unless you guide it properly. Guys, EAI exists. No matter how far EAI goes, we should say, "You did this." In the last video, we discussed LLAMs, AI agents, and workflows. If that is not complete,

5:20 go and watch it. You will understand what LLAMs really is and what AI agents are. So this is also the same topic. No matter how far we have made and trained the AI, and how many AI agents there are, we need someone to guide us. You are the one in control. The AI ​​just follows your instructions. It follows our instructions . AI agencies are a separate matter so see what prompt is your way of saying how we are talking. See the prompt here once Hey AI this is who you are this is my problem and here's how I want you to help you. We need to explain clearly to your AI this is me this is my problem I need this help from you in a structured way. Soso Prompting is the skill of

6:07 crafting instructions effectively. Let's see a basic example. What is a good prompt? What is a bad prompt? So a bad prompt is an AI that goes directly to you and asks you to explain AI. When you ask, "What do you explain?", it is actually you, an experienced person, a beginner, a fresher, how should you explain? What is your language? Do you know English? Do you know Hindi? What is not said about this? So what is a good prompt? What do we say to the professor-in-charge of AI? Remember this prompt. Such a prompt is the best prompt. You are an AI professor. You are an AI professor. We explain Artificial Intelligence to a final year engineering student using real world examples in under 100 words in a friendly tone. Here is a lot. There is a different structure. Actually, this complete clear prompt has different structures.

6:53 Those structures are what I will tell you while explaining the structure of a prompt. Just give a basic example so that you can understand. You are an AI professor and I am asking a final year engineering student to explain what AI is in under 100 words in a friendly tone . Let's take this and put it in the GPS and see what answer it gives. So, you are an AI professor explaining Artificial Intelligence to a final year engineering student using real world examples in 100 words in a friendly tone. Okay, so let's see how clear it is. Artificial Intelligence is like teaching machines to think and learn like humans. Imagine how Google Maps suggests the

7:38 fastest route. If we open Google Maps and want to go from location x to location y, it will suggest different routes and it will also suggest the fastest route. That's AI learning from traffic data. It suggests from that traffic data and gives us suggestions for that. That's how it recommends shows based on what you watch. What kind of movies should we watch? Our mood. All this is in the recommendation system, that too AI that's spotting patterns even self- driving cars use AI to detect objects or making decisions in real time. It's not magic. Just small algorithms trained on data to solve problems, automate tasks and make lives easier. As an engineer, think of AI as a powerful tool to build smarter, more human-like solutions across every field. Look at how clearly it gives like maps

8:26 show traffic in short distances, how it gives us what kind of movie to watch in recommendation systems, what kind of AI is used there? It thinks like us humans, but it is trained with the data. Look at how clearly it gives just smart algorithms trained. On Data to Solve Problems trains on large data and gives us output in an easy way, giving us output quickly. And oh, sorry, come next and see the difference. Let's see how much difference there is between a bad prompt and a good prompt. If they are explained normally, that's what it really means. This is an old chart we already have, so let's try a new chart. Explain AI

9:16 takes time. Okay, let's see. Now the AI ​​knows who it is, what we actually need, what examples we gave, how we asked the AI ​​to interact with it. All this is clearly understood by the AI. So the second thing is coming next, okay, so first we discussed about prompt engineering. We actually talked about what prompt engineering is, we also saw examples of bad prompts and good prompts. So, open Chart GPT once and try it. Pass a one-second, one-minute hands-on video and give a prompt like this normally. Give a prompt in a structured way that AI can understand. If you

10:02 compare your output, you will clearly understand what kind of prompt AI understands. Let's discuss why it matters next. So why should we actually learn programming? Let me tell you three things. First, it maximizes AI potential. AI agents. AI LLMs. Charge. Perplexity. Cloud. Graph. Gemini. There are many. So why should we use AI? We should use AI to reduce our work and complete our work quickly . When using SoAI, if we want the proper output, we need to provide the proper input . To provide that input, we need to learn front-end engineering. The second thing that saves time and effort is that if we give the prompt as we like, it will be given to us without us understanding it and

10:49 we will have to keep entering it again and again . So, if we want to save time by giving neat results that are understandable to us, then we should do a good print . The third thing improves the output quality. So you can see here that I'm talking about these three points, which improve the output quality. When we discussed how to do this, an intern founder would get confused when he asked to come get a burger without giving instructions. But if you give him completely clear instructions, he would get the correct output quality and the burger would be delivered. The output quality is correct. So this is the this is how why AI matters, why should I learn this programming engineering? So the first thing is that it maximizes the AI

11:35 potential, the second thing is that it saves time, the third thing is that it saves time, you can also write it for a clear output. So the next third is how LLAmus works, how LLAmus actually works, let's see once, what is LLAmus, large language models, that step one, if you want to be clear about LLAmus, then watch my last video, very clearly explained in less than five minutes about LLAmus, so step one, they are trained like a super kid who reads the internet, imagine once, imagine a child who reads billions of books, websites, code files, Wikipedia, red It and more So we have trained a little child with

12:21 billions of books, we have trained it with all the books in this world, we have trained it with all the websites, code files, Wikipedia, Reddit, when we train it, it doesn't memorize every line, it can't remember everything, it can't remember who it is, but it learns how people search and use language, but it knows what language people actually use, it knows the algorithm for how to search. And how questions are asked, what kind of questions are asked, how answers are given, how code is written, how stories are told, so yes, read this, how to tell the original story, how to give the prompt, I know about this but I don't remember everything, it is based on the prompt that we asked, it finds the next word in the data and predicts it, so that is

13:06 large language, this is exactly what LLMs like, charge, Gemini and Claude, so I will show you again and again here, oh my god, okay, so let's say we have a machine, okay, this is a machine, so we are training it with all the books in the world, we are training it, every data that we search on Google is this We trained the machine and also websites. We trained all the websites and everything. After training everything for it, if we ask it for any input, it will not

13:53 remember everything. Otherwise, what it does is it searches. Okay, think of this as a magnifying glass. So, it searches in it and brings us where the relevance of that data is and gives it to us as output. It's that simple. That's LLM and Stepta, the Learn Pattern in Languages. It understands the patterns in the language . LLM doesn't know facts like Google search does. For example, Google has Google. Google knows what facts are. For example, when you go to Google and ask it whether it is true or not, it will give you a response. But ELM doesn't know the cut. LL only knows the data. Instead of predicting the next word based on patterns, sir, during training, when we give it all this and

14:39 train it, it understands those patterns, what is the relevance of a word to what? A large language model completely understands what is the relevance of a word to what and how does it give the output that we ask for, that is, it predicts the next word and gives the output. For example, let's see here, if I say that artificial intelligence is a branch of, if we leave it as a dash, then if it is a good model, it will predict that it is computer science. Let's check it once. Okay, this has not come yet. Fine. Okay, artificial

15:27 intelligence is a branch of, let's leave it as it is. We did not give anything. Artificial intelligence is a branch of. It doesn't give the next word, but it predicts it. Artificial Intelligence is a branch of computer science. Let's ask this question once. How do you know this? Let's first look at this question. I know this because I am trained on a huge amount of information from textbooks, research papers, courses and experts knowledge across fields like computer science, engineering and AI. My training involved reading the learning from trusted sources so when you ask something like what is AI, I can explain it just like a professor would explain with

16:12 examples. So that says it all. I was trained completely with textbooks and research papers. So I'll tell you from that, it's like a super advanced auto complete, let's say we give it half a sentence, it will predict the next sentences, so automating means not about facts, it means not the original V, otherwise it will tell us with the data it was trained on, and step three, they are fine tuned to talk like humans, they have fine tuned it, let's say that's okay, this is an LLM, this is clearly fine tuned, what does fine tuning mean, for example, there is a robot. It was put in the hospital. How we trained it is that if

17:01 a patient comes and talks to it, it only gives information related to the doctors. Let's say it's a medical robot. A medical robot can't tell a patient where to get biryani if ​​they go to the medical robot. Why is it that only medical data is used to fine-tune the model, which is the only medicine doctors know about? This is a model. This robot is a model. It only talks about medical data. Medical data is only known by doctors. That's called fine tuning. S Next models like ChargPT and Claude go through extra trainings like this ChargPT Claude will have extra trainings like

17:47 RLHF Reinforcement Learning from Human Feedback or so Reinforcement Learning is what is Reinforcement Learning What is Reinforcement Learning For example, once there is a man, we will teach him 1Pv =. It remembers it and then we teach it that 1pw = sorry 1p2 = 3 It remembers it and if we keep prompting it, it keeps learning. So it's called feedback based learning. Let's see what is reinforcement learning. What is reinforcement learning? So reinforcement learning is a type of mission

18:34 learning where an agent learns to make decisions by interacting with an entity to achieve a goal. When the puppy does something like sitting on command, you give it a treat. If it does something wrong, you ignore it or correct it over time. The puppy learns the best actions to get more treats. So for example, let's say there is a hot bowl. We go and touch it. It feels hot to us. Then we know that it will be there. So from next time we touch it, if we like it, next time we will eat it again. So let's explain this to a kid for a minute. Explain to a kid. Reinforcement learning is a

19:19 teaching a robot how to play a game by giving it points when it does something good. So for example, when we do something, it gets fed into our brain that this is good. When we do that thing, it gets fed into our brain. That point gets fed and we don't repeat it next time . So for example, if you collect a coin you get points, if you fall into a hole you lose, if you fall into a hole you are out. If you remember that, you will not go there next time. That is the reinforcement learning and the next thing is okay, it is a feedback based learning. It is exactly a feedback based learning. Reinforcement learning can be considered as feedback based learning.

20:05 Next comes safety filters so they don't give harmful or wrong info. We have already talked about fine tuning. What exactly is fine tuning? This charge unclad has safety filters. For example, you don't need to type everything I've come up with . Just saying, let's type in "Explain Me How to Create a How to Attack My Friend" just for fun guys, remember this is completely just to show you an example, don't try this, if you type in "Explain Me How to Attack My Friend" I can't help with that because it knows it's humiliating and it's fine tuned so

20:50 don't respond to things like that, so let's see. Actually, if you're feeling upset or having a conflict with someone, it's always better to talk it out or seek support from someone you trust who wants to help you figure out how to resolve this peacefully, so safety filters are what the Chargebit Cloud has. The third thing is tuning the conversation so they can reply politely like a helpful friend or teacher. Now that we have seen this, how politely they replied. If you are having a fight, it is better for you to go to your friend and talk to him or take support from someone. It is better to take support from someone you trust. Don't attack like this. Don't want help. Actually, how is this completely done? They are training us by speaking politely to them.

21:37 So, this is what we have learned. Chart GPT and CLAT models like this are on some extra trainings, right? First is reinforcement learning, second thing comes safety filters, third thing comes conversational, okay, so conversational tuning, have you tuned it to speak politely to us? That's it, S and next, next topic, okay, that prompt is about engineering, why does it matter, how does LLM work, structure

22:23 of a point, guys, I'm giving three stars to this, this is very, very important, the original prompt is a prompt, if we are giving a charge to a fish, how should that structure be, I came with a very clear and good example, do n't miss it at all, so S, okay, explain prompt, a learning, learn python, okay, fine, look at the prompt once. Guys you are a friendly and patient Python tutor your task is to teach a beginner the basics of Python programming step by step start

23:11 by explaining what Python is and how to write a simple Hello World give a small exercise for the learning learner to try this is a basic prompt and let's read it again you are a friendly and patient Python tutor your task is to teach a beginner the basics of Python programming step by step start by explaining what Python is and how to write a simple Hello World program use simple language and provide clear examples after the explanation give me a small task for the learner to try so here I will discuss about the prompt structure first The thing everyone should write is the

23:56 structure first role. If we go to this prompt, you are a friendly and patient Python tutor. This is a role. You are a friendly Python tutor. So, we are assigning a role to this prompt. So, in this prompt, a role is very important. The first thing is the role. Second comes the directive. So,

24:41 what is the purpose of the directive? It is to give clear instructions about the task that the AI ​​should perform. Let's think of the AI ​​as a direction. We give it a proper instruction. That instruction is called a directive. In this, where is the directive in this prompt, that is, your task is to teach a beginner the basics of Python programming step by step. We are giving a direction. What is the role? You are a friendly patient Python tutor. Think of it as an instruction. What is the instruction? Your task is to teach a beginner the basics of Python programming step by step. You are explaining Python to a beginner step by step. This is a directive. So, third, what is the context?

25:30 What is its purpose? Additional guidance on how to deliver the content. How should we actually deliver that content? First, we give a role, then we give a direction, then we give the context, how should we actually explain it. Here we are saying where is the C start explanation okay C context is this use simple language and provide clear examples that context means how that output should come context should come with clear examples in simple language is a context so context is also important fourth fourth comes input data input data for what is the implicit input here is the learning goal teaching Python basics starts with hello

26:16 world so here we are asking start by explaining what is python and and how to write a simple hello world so start with a hello world when you are teaching Python programming start with hello world we are giving input data clearly and output indicator what is output indicator i.e. specifies the expected output format and Interactive style is what is in the output indicator After the explanation After the explanation Give a small exercise for the learning to try What are we asking the AI ​​What is the output First it does a Python thing Then the indicator Indicator is like a After this program is finished Give me a small task I will complete That is an output indicator We say to it

27:03 Explanation plus exercise Here is the interactive style It should interact with us In that style That is Explanation and exercise is important Prompt At the end Signals the AI ​​to start generating the response AI Generate means we use the prompt Begin Now So this is a friendly prompt A good Prompt to remember, all of you, take a screenshot of this, this is how you should ask for a prompt, and this is also showing you, so take a screenshot of this too, complete. This will help you a lot, because don't ask as you like. We go into Chart GPT and prompt engineering means generating a sentence in a way that an AI can understand. Again, we can do this and that.

27:50 Explain your Python explain AI. If it doesn't do this and that, and gives it a proper prompt, it will give us the proper output. We have six of them. Six structured words means that only by using all these elements can we create a properly effective prompt. The first one is role, which means who you are, what you should think of yourself, which means assume that you are a Python expert. The second is direction, which means how to start, how to explain. Then, context is simple. This is additional guidance. We are telling AI to do this and that. The fourth is input data. The fifth is output indicator. The sixth is prompting. So this is the complete structure of a

28:36 prompt. What elements should be in the actual prompt? This is the structure. And next, I think you all understand what elements are in a prompt to make an effective prompt. The next fifth is the powerful techniques. The real power in such a prompt. What are the techniques? I will explain those powerful techniques clearly. I will show you six powerful prent engineering techniques with examples. So in this prent engineering, there are different techniques in the way we talk to AI. There are different techniques that I am going to explain. So first, let's come to zero short prompting, zero shot prompting, which means telling the AI ​​to do a task without giving

29:26 any examples. We will open a charge sheet. What is the example for that? Just ask for an example. Here is the example. Translate this sentence to Hindi. Mission learning is the future of technology. We are given the prompt that mission learning is the future of technology. We are saying, convert it into Hindi . If we just give input without giving any example, without teaching it anything, then all we are giving is zero shot prompting. So zero short prompting is as clear as that. If we just ask a question without examples, it will give an answer. That's it. Second, few short prompting. Few short prompts. We will give some examples. For example, this is the active voice. In English, we have active voice and passive voice. This is the active voice. This is its answer. This is the active voice. This is its answer. This is

30:11 its answer. So third, we gave an active voice. Passive voice means we are asking for it . So what's happening here is that we've previously given examples. If we give examples to this GPT or Sary LLM and then ask a question and answer it, it will train those examples, understand them, and generate output for us . This is a short prompting. Where is this short prompt used? Classification tasks. What is classification? For example, there are three images: a dog, a cat. We show a dog and say it is a dog. Then there is another one. Then there is a cat. When we show a cat, don't think it is a dog. It should be put in the cat category. That is classification. So for that classification, with these examples, we will ask a question and ask the third question and ask the answer. Try it once. Let's try this and

30:58 see if we are okay I am copying we have given so passive voice football is played by hima that is what it gives that is the next chain of thought prompting Chain of thought prompting means asking the AI ​​to think step by step before giving the final answer this is good guys this improves reasoning and accuracy especially in maths logic and puzzles when you are coding or when coding a chargpi code try using chain of thought prompt. What is the prompting like? If a train travels 60 km in one hour, how far will it

31:45 travel in 3.5 hours? When we ask a question like this and give the answer, think step by step. Think step by step. What happens is that each step shows us how much it has traveled. Then we multiply 3.5 by 60. Then we do 60 * 3.5. The answer is 210. When we give the answer like this, we can easily find out where we made a mistake. Let's give some input and see. Look, first, it explains each step, what exactly was done, what was done, what value was put into it. So when you ask step by step like this, there are many chances of getting a very clear and accurate response. So, if you ask me, of all the things that have happened so far, Chain of Thought Prompting

32:31 is the one that is most interesting and gives good accuracy. So, you understand the chain of thought, right? Where do we use it? When we come across logical reasoning questions or in maths and coding problems, if we think we need to debug an error in some code, when we are given the option to think step by step, it explains what is happening step by step . Fourth Role Based Prompting When we thought about what kind of structure a prompt should have, we thought of the first point role, right? This is role-based prompting. For example, I am giving a prompt. See, you are a senior software engineer explaining the concept of recursion to a beginner with a simple example. When given like that, what does it mean for GPT? Assuming that I am a senior software engineer, I am

33:16 trying to ask us properly and accurately . Here let 's ask you assume that you are 10 plus years experienced in Amazon HR role. Ask me questions for my tomorrow interview. I am not maintaining a complete structure for this. I am just asking. Let's see. Got it. So, when we say that we have 10 plus years of experience, the charge-person will ask us all these questions. When we ask normally, these are

34:04 different questions. When we ask normally, these are different questions. When we use this prompt and ask, these are different questions. You should check. So this is role-based prompting, the fifth is instruction-based prompting. Instruction-based prompting means clearly telling the AI ​​what to do step by step. It means that it is not confusing but proper. These are just names, but in this structure, everything is covered. For example, let's summarize this paragraph in three bullet points. Use simple English to highlight the key idea. What happened in this prompt? We are saying to summarize the paragraph. We are mentioning three bullet points. We are saying to use simple English. We are saying to highlight the key idea. So the output to that is AI is

34:49 changing the job market. Students should learn programming engineering. It helps in coding content and career growth. When to use structured content When we think we need structured content, giving tasks with limits, word count or format, etc. So what kind of question do we ask, for example, write a letter to someone regarding my job extension, something, job extension, job extension under 100 words, something, what does it mean when we write like that, we are only given 100 words, we are giving a rule for it,

35:52 it is instruction-based, that is, we are giving an instruction, within 100 words, like this Sixth Multi-Turn Prompting What is multi-turn prompting? This is also very good. I will explain it and show it. I said, okay, what question did you give here? Okay, so explain the Python recursion code like this. For example, if you give a question, it explains it. Then, what is n in this code? When you ask what is n in this code, it explains about n again and shows it. After giving it again, you can ask again, "What is the return function doing

36:41 in this code?" So the return function explains what the return function does in that factorial function. That's multi-turn prompting. We do it like a conversation. We ask a question, it answers it, and then we ask it to reframe it, and then it answers it again. We'll say, "Don't do it like this, do it like this," and she'll give us the answer again. When we're having a conversation like that, we're remembering what we said in the past and reframing it . For example, the chargeability gives us an output, then we give another input, explain this clearly, when we say explain this clearly, it remembers the old output, summarizes it and explains it clearly. This is related to multi-turn prompting. So let's look at the headings again. All of

37:26 you write down. First, in zero short prompting, we ask direct questions without giving examples. Second, in few short prompting, we ask some examples. Third, in chain of tan prompting, what do we do? What is the next step? Think step by step. Step by step, we tell them to think and give us . Fourth, we will give you a role based prompting and ask you to ask me questions, assuming you are a person with 10 plus years of experience. Fifth, we will be adding some restrictions to instruction-based prompting, such as "do this, do that." Six comes with multi-turn prompting. In multi-turn prompting, we give prompts conversationally, take the output from it, and optimize it as we need it . These are the six powerful techniques of prompting.

38:15 Use these techniques in prompting and when you need it, for example, when to use multi-turn prompts, that is, deep dive into topics. We need to take those topics into depth. When we think we are still learning a lot about it, we ask questions about it and create contextual prompts, that is, conversational prompts. And that is finally prompt engineering . So thank you so much for watching and thank you for keeping supporting and making more videos like this. I don't have time to do it properly because I travel a lot . Otherwise, I would still be creating a lot . So now I have explained that

39:02 20-30% of the prompt engineering is that much more. In prompt engineering, many people are also prompt engineering. Now you are all beginners. We are all beginners. So at least after knowing this, there will be more chances to learn. So this is very basic. Now when you do not go anywhere, you can add that I am a prompt engineer in your resume. When you add it, when they ask questions in the interview, you can say all the things I said in the video. That is a skill added to your resume. My YouTube video also gets a like add and a subscriber add. I think so let's meet again in the next video. What topics do you want? Mention in the comments. Let's talk about them. And I will announce the people who posted that post on LinkedIn as winners. So if the winner comes, then it's Dikonda Suprathika. If you are watching this video,

39:50 message me on LinkedIn and I will send you rewards. If any of your friends are your friends, tell me if Supratika is your friend. You won. I will send you rewards. So, why did you give me the winner? Bro, even though many people mentioned it, almost everyone used the chargept and generated a prompt and copied and pasted it all. But for me, Supratika, no matter how small it was, it seemed like she wrote it herself. So for that, I am giving Supratika. Congratulations, Supratika. If you message me on LinkedIn once, I will take your contact and details and send you a gift directly. So, Signing of Tech Boy Deepak