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Hello all my name is Krishna Ayak and welcome to my YouTube channel. So guys here is one amazing one short video on Langchain in order to learn generative AI. So if you are interested in creating amazing LLM application or Gen AI powered application then this specific video is definitely for you. If you don't know ab... | |
The most interesting thing will be that I will be covering all the paid LLM models along with that open source LLM models even though that they are hosted in Hunging Face. So we will be getting to know each and everything about it and how we can specifically use in Langchain. So I hope you enjoyed this particular vid... | |
Please make sure that you hit like, share with all your friends and we will also keep a target of 200 comments. 200 comments I know you will be able to do it. So let's keep that specific target and let's understand what all things we are going to learn about Langchain. And then we will also understand the second top... | |
Now, right now in the Langchain documentation if you probably see the recent updates that are there. Mainly most of the modules revolve around this particular topics in Langchain. Okay. So over here you will be able to see Langsmith. Here you will be able to see Langsr. If I talk about Langsmith, recently I had al... | |
So Langsmith is, if I probably give you some examples, it will help you to monitor your application. It will help you to debug your application. So in short, whatever MLOps activities is specifically required with respect to monitoring, deploying, debugging, testing. You know, so third point I will say testing. You... | |
The best thing will be that all the reports, all the analytics you will be able to see very much easily in this ecosystem itself. In the Langchain ecosystem. So there is a dashboard in Langchain which you will be able to see it. Okay. Now how we are going to use this entire technique in some projects, we will be se... | |
So this part that is right now required in many, many companies. So that part will also be able to cover it. That is the reason why I like Langchain. Because it is providing you the entire ecosystem. Irrespective of any LLM model. Okay. Any LLM model. Now coming to the second thing over here, you have Langsr. L... | |
You can write the code from scratch with the help of Flask or some other libraries. But here Langsr uses something called as Fast API. Okay. And because of this Fast API, you know, creation of this particular API becomes very much easy. So we will be also able to understand it before the deployment. If I have actu... | |
Now coming to the next thing. There are some amazing concepts and important concepts in Langchain. From data ingestion to data transformation and all. In that, major, major topics are with respect to chains. We will try to understand about chains. And probably in the next video, once I probably start the practical... | |
Now in this LCL, we will be discussing and this full form is Langchain Expression Language. Right. So there are a lot of concepts that are specifically used in LCL. We will also see that how it is basically important while you are building. What are techniques it actually has when you are creating your own generati... | |
These are concepts that you should really know. The main aim of this entire series is not to make you understand in theoretical concept. But it's more to understand how you can create amazing generative AI applications. Irrespective of any LLM model. Now see guys, one of the questions that I get from many people. ... | |
Right. Can you show me some examples with respect to open source models? Like Google Gemini or some other, Mistral. What about open source models? Right. See LLM over here. As I said, Lantern is an amazing framework to build the entire application. And creating the best LLM is already a rat race. So if I probab... | |
So you don't even have to worry about this. Right. Whatever models will be probably coming up in the future. Don't worry about this. The main thing is that how you can use this LLM app in a generic way to build any kind of application. So here, whatever model it may come, you just need to stay up to date. Right. ... | |
Like what are the very important things. So this is the diagram that I have already taken from the LLM chain. So as I already discussed, with respect to LLM, the first model that you see over here, we basically say it as observability. Right. And with the help of LLM, you will be able to do debugging, playground, ... | |
The next thing is with respect to deployment. And recently, right now, LangServ has actually come. It will be in the form of API. So here it is written, right? Chains as REST API. Whatever services you are specifically providing. LangChain soon will also come up with only one click deployment mechanism. Once you... | |
In this entire series, I am going to again start from fresh, combine new topics and create a project. Okay. The third thing that you really need to understand is about templates. Okay. So there are different, different templates, reference application. With respect to LangChain, you need to understand these three ... | |
Because in some of the article, it was already said that AGI application is going to get created in Python. Okay. Then you also have some integration components. So we are understanding about the ecosystem. Because all these things we are going to discuss in this specific playlist itself. So here you have ModelIO.... | |
In ModelIO, you have various techniques with respect to Model, Chain, Prompt, Example, Selector and Output Parser. And finally, you'll be also seeing that we'll be focusing on this amazing thing which is called as Protocol. Which is basically coming under LangChain Core. And here we are going to discuss about LangCh... | |
And then we will be focusing on end-to-end projects. How you can use all these concepts together. Right. Understand one thing guys. When we use LangServe and start creating REST APIs. We will also be writing our client side code. So that we will be able to access those kind of APIs. Right. So all these things i... | |
Again why I am saying this is important. Because tomorrow whatever LLM models may come. Right. How advanced it may be. LangChain will be a generic framework. Which will actually help you to build any kind of LLM application. In this video I will be showing you how you can create chatbot applications. With the he... | |
One way to basically integrate any open source LLM is through Hugging Face. But as you know that I am focusing more on the LangChain ecosystem. And with respect to Hugging Face I have already uploaded a lot of videos in my YouTube channel. And how you can actually call this kind of open source LLMs. But since we ar... | |
And obviously my plan is that this month I will be focusing entirely on LangChain. Many more videos will be coming up. Many more amazing videos. Along with end to end application. Fine tuning. Many more things is going to come up. So please make sure that we will keep a like target for every video. And for this ... | |
Please make sure that you subscribe the channel. And take up membership plan from my YouTube channel. So that it will help me. And with the help of those benefits. I will be able to create more videos as such. So let me quickly go ahead and share my screen. So here is my screen over here. And you will be able to... | |
Is the real practical implementation. That is probably there. So as usual. The first thing that we are going to do. Is that create our venv environment. How to create it. Condact create minus p venv. Python is equal to 3.10. You can probably take 3.10 version. And I have already shown you. How to create virtu... | |
The second one is OpenAI API key. And Langchain project. You may be thinking. This OpenAI API key. I have kept it as open. No it is not. I have changed some of the numbers over here. So don't try it out. It will be of no use. Okay. And then. The third environment variable. That I am actually going to create... | |
I will be able to see. Observe the entire. I will be able to monitor. Each and every calls. From the dashboard itself. How we will be using this. Everything I will be discussing about it. Okay. So all these things. Will specifically get required. And. All this will be used. In our environment variable. So ... | |
With the foundation model. Later on. This complexity. Will keep on increasing. So let's go ahead. And start our first code. Now. What is our main aim? What we are trying to do. In our first project. Let me. Just discuss about. Because these are all the things. That we are going to discuss. In the future. ... | |
Chatbot. With the help of both. Paid and open. Open source. LLM model. So this will be the chatbot. That we will be creating. One way. Is that. We will be using. Some paid LLMs. Now paid LLMs. One example. I can show it. With the help of. OpenAI API. Okay. OpenAI API. The second one. That I will try ... | |
That you can do. And one more. I will try to use it. With the help of. Open source LLM. See. Calling. APIs. Is a very easy task. Okay. But. The major thing. Is that. Since we have. So many modules. We are going to use. Langchain. As suggested. Right. And in. Langchain. We definitely. Have so many ... | |
Dependencies. We have specifically. Right. Dependencies. Now. If you probably. See this diagram. Here you will be able. To see. There will be. Model. Prompt. Output. Parser. So. In our video. In this video. I am going to see. Some of the features. With respect to. Langsmith. I am going to see. Som... | |
All the projects. That we are doing. Entire. Videos. That are probably. Going to come up. Will be much more. Practical oriented. Okay. So. Now. Let's start. Our. First. Chatbot application. So. Here. I will go ahead. And write. From. Langchain. Okay. From. Langchain. Underscore. OpenAI. Since.... | |
Basically do. From. Langchain. See. This. Three things. Will definitely be required. Then. One is. Chat. OpenAI. Or. Whatever. OpenAI. Your. Whatever. Chat. Model. That you are going to use. How to call. Open source. I will also be discussing. About that. First of all. We will start with. OpenA... | |
Chat. Prompt. Template. Okay. At any point of time. Whenever you create. A. Chat. Bot. Right. This chat prompt template will be super important. Right. Here is what you will. You will basically. Give the initial prompt template that is actually required. Okay. The third library that I am actually going ... | |
Output. Parser. Okay. Now this three are very important. This string. Str. Output. Parser. Is the default output parser. Whenever your LLM model gives any kind of response. You can also create a custom output parser. That also I will be showing you in the upcoming videos. Okay. This. Custom. Output. Par... | |
But by default. Right now. I am going to use just str. Output. Parser. Now along with this. The next thing that I am actually going to do is that I am going to use streamlet as st. Okay. Streamlet as st. Then I am going to also import OS. And since I am also going to use from .env import load underscore .env... | |
So let's see whether everything is working fine or not. Okay. From .env. So here I am going to basically write python load underscore . Sorry. Python app.py. I am just running it so that everything works fine. And all our libraries will also get in there. Cannot. Python app.py. Okay. I have to probably go to... | |
So now I will clear my screen. Python app.py. Sorry. From streamlet as st. Okay. Import streamlet as st. I have to write. So that is the reason it was coming all these errors. Now let's see if everything is working fine. Langchain core. So here you can probably see that there is a spelling mistake. Okay. | |
But I am just going to keep all the errors like this. So that you will be able to see it. Python app.py. If everything works fine. Dot output parser. Okay. P capital. So I think my suggestion box is not working well. And that is the reason. Now everything is working fine. Here you can see that I am not gettin... | |
Since we are going to use three environment variables. One is the OpenAI API key. Langchain API key. And along with that. I will also make sure that. The tracing. To capture all the monitoring results. I will keep this three environment variable. One is OpenAI API key. Langchain tracing version 2. And Langcha... | |
And tracing. We have kept it as true. So it is automatically going to do the tracing. With respect to any code that I write. And this is not just with respect to paid APIs. With open source LLM also. You will be able to do it. Now this is the second step. That I have actually done. Now let's go ahead and defin... | |
From underscore messages. Okay. And here I am going to define my prompt template. In the form of list. The first thing. That. With respect to my prompt template. That I am going to give. Is nothing but system. And system. Here I say that. You are. A. Helpful. Assistant. Please. Respond. To. The querie... | |
Please respond to the questions or queries. Please respond to the user queries. Okay. Whatever queries that I am going to specifically ask. A simple. Prompt. That you can probably see over here. The next statement. After this. Is what. So. This will be my next. See. If I am giving a system prompt. I also ... | |
I will define something like. Question. Colon. Question. I can also give context. If I want. But right now. I will just give it as a question. A simple chatbot application. So that you will be able to start. Your practice of creating all these chatbots. So now. I will go ahead and define my streamlet framew... | |
So here. I am going to basically write. St. Title. Langchain. Demo with the OpenAI API. St. Text. Input. Search the text topic you want. Okay. Now. Let us go ahead and call my OpenAI LLMs. Okay. OpenAI LLM. So here. I am going to basically write LLM. And whenever we use OpenAI API. So it will be nothi... | |
So I am going to use. Turbo. Because the cost is less for this. I have. I have put $5 in my OpenAI account. Okay. Just to teach you. So please make sure that you support. So that. I will be able to explore all these tools. And create videos for all of you. Okay. And finally. My output parser. See. Always... | |
And next one is the output parser. Obviously. This is the first thing that we require. After this. We integrate with our LLM. And then finally. We get our output. So string output parser. Is responsible. In getting the output itself. Finally. Chain is equal to. We will just combine. All these things. So h... | |
If input underscore text. Colon. Now. Whenever I write any input. And probably press enter. Then I should be able to get this output. So st.write. And here. I am going to just write. Chain.invoke. And finally. I get. I give. My input as question. And that input. Is assigned to my input text. Input text.... | |
St.write. Now. This is what we are doing. A simple chatbot application. But. Along. With this. We have implemented. This. This. This feature. Is specifically for. Langsmith. Langsmith. Lang. Smith. Tracking. Okay. This will be amazing for. To use. Okay. And. This is the recent updates. That are t... | |
Now let's go ahead. And run this. So. In order to run it. You will just need. To. Write. Nothing but. Streamlet. Run. App. Dot. P. Y. Okay. Oops. There is an error. App. Dot. P. Y. And. Here. I will do. Allow access. Okay. So right now. You will be able to see. Over here. Langchain. Series... | |
Okay. And just press. Enter. You will be able to see that. We will be getting this information. Over here. And here. You can see. My. Project. Something. Let me reload it. Tutorial 1. Right. So this is the first. Request that is already been hit. And here. You will be able to see. Your. Unable sequen... | |
What was the cost. Everything. You are able to track. So .00027 dollars. Is the. Cost that. Actually took. With respect to this. And finally. My string. Output parser. How can you assist today. With respect to this. Output parser. It is just going to give me. The response. Clearly. Now. When I develop... | |
A python code. To swap two numbers. Okay. So once I execute this. And here. You will be able to see that. I am able to get the. Output. And answer. Everything is over here. And for this. You will be able to see. The cost will be little bit high. Okay. If you don't agree with me. Or. Let's see. With res... | |
It is based on the token size. Right. For every token. It is bearing some kind of cost. Perfect. This was the first part. Of this particular tutorial. Now. Let's go to the second part. The second part. Is more about. Making you understand. That. How you can call. Open source LLMs. In your local itself. ... | |
The best thing about Olama. Is that. It automatically. Does the compression. And probably. In your local. You will be able to. Run it. Let's say. If you have 16 GB RAM. You will. Just have to wait. For some amount of time. To get the response. But. Lama 2. And Code Lama. You can specifically. Use it o... | |
Both in Mac. Mac. Linux. And Windows. Wherever you want. Just download it. After you downloaded it. What you really need to do. Is just go ahead and install it. It is a simple. Exe file for Windows. MSI file for Mac OS. And Linux. Is a different version. So. You just need to double click it. And start i... | |
And create another file. Local. Llama. Okay. Locallama.py. Now locallama.py. What we are going to basically do. Over here. Is that. With respect to the local llama. I will first of all. Go ahead and import. Some of the libraries. See. Code will be almost same. Right. There also. I will be using. Chat ... | |
Now along with this. What I am going to do. I have to import. Olama. Right. Because that is the reason. Why we will be able to. Download all the specific models. Okay. So. Langchain. Community. LLM. See. Over here. Whenever we need to do. The third party integration. So that will be available. Inside ... | |
And then we have this. Output parser. String output parser. Core. Prompts. That is nothing but. Chat prompt template. And everything is there. Okay. Now. Let's go ahead. And write import. Streamlet. As. St. So. I am going to. Going to use the streamlet. Over here. Along with this. Import. OS. And.... | |
Load underscore. Dot. Env. Okay. Now. We will initialize it. Load underscore. Dot. Env. Okay. Once we initialize all this random. We are. All this. Environment variables. As usual. I will be importing this three things. Now see. In my previous code. When I was using OpenAI API. Prompt template. We h... | |
You really need to understand. How with the help of Olam. I can call any open source models. Okay. So. Here it is. And then finally. You will be able to see. Where is my. Code to call my. OpenAI LLMs. That we are going to see over here. So. This is done. Now. Streamlit framework. Also. I will try to ca... | |
But here we are calling. Chat OpenAI. Okay. I specifically don't want Chat OpenAI. Instead. I will be calling. Olama. Okay. So. Olama. Whatever library we have imported. So. Olama. Okay. And then. Here we are specifically going to call. Olama2. Okay. Now. Before calling any models. Now. Which all m... | |
Dolphin5. 5.2 Neural Chat. Code Lama. All are mostly open source. Gamma. Gamma is also there. But before calling this. What you really need to do. Is that. Just go to your command prompt. Let's say that I want to use Gama. Gamma model. Okay. So. What I have to do. Or I have to use Lama model. Right. So... | |
Some location. There will be there. We have to download that entire model. So. Let's say that. I want to go ahead and. Write. Olama. Run. Gamma. So. This. What will happen. It will pull the entire gamma model. Right. Wherever it is. So. Here you can see. Pulling. Will basically happen. Now. This is... | |
Then only I will be able to use the. Gamma model in my local. With the help of Olama. So. I hope you have got an idea about it. Now. What I am actually going to do. So. Here I have called. Olama. Model. Lama 2. Okay. Then again. Output parser is this. And I am combining prompt. LLM. On output parser. ... | |
Has a 64 GB RAM. It has. NVIDIA Titan RTX. Which was gifted by NVIDIA itself. So. With respect to this. Amazing system. I will be able to run. Very very much quickly. That is what I feel. So. Let's go ahead and run it. So here. What I am actually going to do. I am going to write Python. So. It is stream... | |
Now. Instead of. Open AI API. I should have. Okay. No module named Langchain Community. Let's see. Where is Langchain Community. Okay. I have to also make sure. That in my requirement. Dot. TXT. I go ahead and. Use this. Langchain Community. And I need to import. This library. Since. I need to do tha... | |
CD. Dot. Dot. Okay. Now. If I go ahead and write. Pip install. Minus. R. Requirement. Dot. TXT. So. Here you will be able to see. My requirement. Dot. TXT. Will get installed. This Langchain Community. Will get installed. Once I am done with this. Then I can probably. Go ahead and run my code. Ok... | |
Once this is done. Then what will happen is that. We can. And you can use any model. Up to you. Okay. And I don't want this. OpenAI key also. Only this two information. I specifically want. I will be able to track all these things. Okay. And later on. I will also show you. How you can create this. In the... | |
That people are doing. The company is doing. Amazingly well. In this open source world. And it is developing. Multiple things over there. So. Now. I will go ahead and write. CD. Chatbot. I will go inside my chatbot. And then. I will run this. Python. Local. Llama. Py. Once I execute this. Now. I don... | |
Not Python. Run. Streamlet. Run. Now. Here you have. Again. I will be getting. OpenAI. Text. Over here. Let me change this. Also. So. That I can make it. Perfect. With. Llama. Two. Okay. So. I have executed it. Saved it. I will rerun it. I will say. Hey. Hi. So. Once I execute it. You will ... | |
How can I help you today? Now. If I probably go ahead. With respect to this. Dashboard. Let's see. Where it is. So. Now. Tutorial One. You will be able to see. That. This will increase. Okay. There will be one more. Over here. Right. I have reloaded this page. Okay. And you will be able to see it. O... | |
Right. So. Here you will be able to see. If I extend this. There you will be able to see. Chat prop template. Llama. Llama is over here. Now. This. Llama is specifically. Calling Llama 2. Over there. And. Whatever open source libraries. That you specifically want. Just to call this. It is very much sim... | |
Provide me a python code. Python code. To swap. Two numbers. Okay. If you want more. Coding well. Chatbot. You can directly use. Code Llama. If you want. Okay. So here you can see. All the examples are there. And this was quite fast. Right. So this is good. You know. So if you have. The right kind of... | |
And all. Right. Hello guys. So we are going to continue. The LangChain series. Already in our previous video. We have already seen. How to create chatbots. With the help of. Both OpenAI. API. And open source LLM. Models like Llama2. We have also seen. What is the use of. O Llama. How you can run. All t... | |
Which is very much important. For our production grade deployment. That is creating APIs. You know. For all this kind of. LLM. Models. We will be able to create APIs. And through this. You will also be able to do. The deployment. In a very efficient manner. Now how we are going to create. This specific API... | |
A Swagger UI. Which is already provided. By LangChain. The LangServe library. That we are specifically. Going to use. Now it is important. Guys. You know this specific step. Because tomorrow. If you are also. Developing any application. You obviously. Want to do the deployment. For that particular applica... | |
How we are going to go ahead. First of all. I am going to show you. The theoretical intuition. How we are going to develop it. And then we will start. The coding part. So let me quickly. Go ahead. And share my screen. What we are actually. Going to do over here. Over here. You have seen. That I have writt... | |
This applications. Are obviously created. By software engineers. Right. It can be a mobile app. It can be a desktop app. Web app and all. Now for this particular app. If I want to integrate. Any foundation model. Or any fine-tuned foundation model. Like LLMs and all. So what I really need to do. Is that I ... | |
I want to use those functionality. Along with my web app. Or a mobile app. So what we are doing. Is over here. Is that. We will create. This specific APIs. Now this APIs. Will be having routes. Okay. Routes. And this routes. Will be responsible. Whether we have to. Probably interact with OpenAI. Or whet... | |
Paid API models. Specifically for LLM. We can definitely use it. Now this is what we are going to do. In this video. We will create. This separately. We will create. This separately. And at the end of the day. I will also give you an option. Through routes. How you can integrate. With multiple LLM models. ... | |
The reason why I am making this video. Understand one thing guys. Because. In LLMs also. You have different performance metrics. Some model is very good at. Some performance metrics over there. Like MMLU. Other metrics are definitely there. So this is an option. Where we can use multiple LLM models. Now what... | |
We have created this first folder. That is Shared Bot. And inside the Shared Bot. We had created app.py. Then local llama.py. Right. We did this entire thing. And as I said. Every tutorial. I will keep on creating folders. And developing our own project over here. Now. Let us go ahead. And create my second... | |
With this local llama. Or app.py. Right. Over here. I have used openai. API key. Here I have used open source models. So we will try to integrate. Both of them. In the form of routes. Okay. So that we will be able to create an API. So let me quickly go ahead. And write over here. App.py. So one will be m... | |
Because we are going to integrate this APIs. With this mobile app. Or web app. Okay. So quickly. Let's do this. First of all. I will go ahead. And start writing the code in. App.py. Before I go ahead. And we have to make sure. That we need to update. All the requirement. Almost all the libraries. I have ... | |
I will be using it. So. First of all. I will go ahead. And install all these libraries. That will do. Once we run the code. Right now. Let's go ahead. And write my app.py code. Okay. Now as usual. First of all. Let me just open my terminal. Okay. And let me do one thing. With respect to the terminal. I... | |
Okay. First of all. I need to just write cd. Dot dot. Okay. Then I will clear the screen. And then go ahead. And write pip install. Minus our requirement.txt. Now here you will be able to see that. My entire installation will start taking place. And here. The other three packages. That I have actually writ... | |
API. Import. Fast. API. Okay. So this is the first. First library that I am going to import. Along with this. I have to also make sure that I create or I import my chat prompt template. So from. Langchain. Since we are going to create an entire API in this. Okay. Dot prompts. Import. Chat prompt template... | |
So this is done. Okay. So this is done. Then. From. Langchain. Dot. Chat underscore models. Import. Chat. Open AI. So this is the next one. Since I need to. Make sure that I need to. Create a chat. Chat application. So that is the reason why I am using this chat models. Okay. | |
This is the next library that I will be going ahead and importing. Along with this. I will also use Lang serve. Which will be responsible in creating my entire APIs. Right. So from Lang serve. Import. Add routes. Okay. So through this. I will be able to add all the routes over there. Right. Whatever routes.... | |
Import. Uvcon. Okay. Uvcon will be required over here. Oops. Okay. Next is import OS. See I can probably enable my GitHub copilot. And I can probably write the code. But I don't think so. That will be a better way. I usually use this AI tool. Which is called as black box. So that it will help me to write m... | |
It also explains about the code. So I will probably create another video about it. Okay. So how to basically use this. Then one more thing that I really want to import over here is. My olama. So from langtion. Orisco community. Dot llms. Dot llms. Import. Olama. Okay. So this is done. All the libraries th... | |
Now this is done. Now what I am actually going to do over here is that I am just going to write OS.Environment. And first of all I will initialize my OpenAI API key. So I will write OpenAI underscore API underscore key. Okay. And this specifically I will load it from OS dot get env. And then I will go ahead and w... | |
So this is the first thing that we really need to do. Before I do this I will just go back to my app dot py. And I will just initialize this. Okay. Load underscore dot env. Let me quickly copy this entire thing. And paste it over here. And I will initialize this. Load underscore dot env. Okay. | |
So this will actually help me to initialize all my environment variable. Perfect. I have also loaded my OpenAI API key. Now let's start this fast API. Now in order to create the fast API I have to create an app. Here I have given title Langtian server version 1.0. And the third information I basically want is a d... | |
So what I will do add underscore routes. And this routes is basically to add all the routes over there. Right. So the first time when we are adding this particular route. So you have to make sure that I give all the information like whether I am going up with my app. Let's say I go with this chat OpenAI API. Chat... | |
So this is my one of my route. You can just consider this OpenAI route. And this is my model that I will specifically be using. So this is just one way how you can actually add route. Okay. But let me just add some more things. Because see at the end of the day when we created our first application. We used to c... | |
I also integrate my prompt template with it. Okay. So here I am going to say model is equal to chat OpenAI. Chat OpenAI. And I am going to initialize this particular model. And then let me go ahead and create my other model also. See O Llama. I will just use my Llama2. Okay. So this model also I need to basica... | |
And this will basically be my model is equal to. Llama2. Okay. So I am going to use the Llama2 model. So this is my one model over here. This is my another model over here. Okay. Now let me quickly go ahead and create my prompt one. So my prompt one will be my chat prompt template. Chat prompt template dot fro... | |
And here I am going to basically give one chat prompt. Okay. Let's say one of my interaction. One. I want to use OpenAI API. For OpenAI API. Let's say I want to create an essay. So I will say write me an essay. Write me an essay. Okay. About a specific topic. That topic I will be giving it. Okay. About som... | |
Around with 200 words or with 100 words. Okay. So this is my first prompt template. Okay. I am saying this will be my prompt template. Write me an essay about whatever topic I give with 100 words. Okay. Something like this. So this let's go ahead and write this. This is my first prompt template. Then I will c... |
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