Prompt Engineering And LLM's With LangChain In One Shot-Generative AI

hello all my name is krishnaik and welcome to my YouTube channel so guys in this video we are going to probably discuss about prompt engineering using Lang chain now property engineering can be super important if you're probably working with llm models because it will actually help you to design the input to the model that you are having a conversation with so obviously uh in my previous video we have discussed about Lang Chen and I also showed you that how we can actually create a simple Search application just like a chatbot using Lang chin now let's go ahead and let's discuss about prompt engineering so um in prompts engineering we specifically designed the prompts itself right so if I talk about Lang chin there is a topic which is called as prompts uh over here you can see the new way of programming model is through prompts a prompt refer to the input to the model this input is often constructed from multiple components so here in launching you will be able to create your own prompts using this prompt template right so it is responsible for construction of the inputs Langston provides several classes and functions to make constructing and working with prompt easy so obviously whenever we use different tools I can use land chain there are also other tools like chain lit over there also the prompt template can be used from the land chain itself right so let's go ahead and let's quickly start the coding over here and here I'm going to give you a lot of examples with respect to this so um as usual so over here the first thing that you really need to do over here is that go ahead and use the open AI key the serp API key I will talk about it later on because let's say if you're probably creating a chat bot and you want to also use Google search in that we can actually use the Google search API so for that purpose I'd actually use that but initially we just require this open API key I have already loaded it away and executed it but whatever API key you specifically have you start with this okay as discussed earlier we are going to specifically use prompt prompt template from the Langston itself so now let's go ahead and import from Lang chain okay and many people are asking what are the prerequisite prerequisite is nothing it's just python if you know python everything will be working in this specific way you just have to keep on looking on the Langston documentation with respect to the syntax and the parameters that we are going to use okay so from Langston we are basically going to import The Prompt template okay and here from this prompt template I'm just going to use template first of all we need to design some kind of template so one example that I am probably going to probably take over here will be something like this so see over here I will be designing my own from template OAC so I'm just saying that I want you to act as a financial advisor for the people in an easy way explain the basics of whatever topic I really want to give right from the search engine itself let's say uh I want to understand with respect to a financial concept what is income tax so that income tax word will get replaced over here so this is a generic template that I specifically need to use it you can change it anyhow like whatever way you specifically want like question answers different different ways you can basically do it I'll show you more examples as we go ahead right now once this is done once the template is basically decided the next thing that you specifically need to do is that use this prompt template and create your entire you take this specific template and insert it over here now in the prompt template the first thing is that you have to Define your input variables now what all input variables you are specifically providing over here that is nothing but Financial concept right so I will be writing the financial concept over here and make sure that you are always write this in courts so whatever parameters you're giving from your end that parameters only you have to put it away you can if you are providing one parameter just provide one parameter as your input or two parameters provide two parameters as input then in the next statement that we specifically need to write over here after the input variables right is nothing but your template right now what template I am basically using for Designing my input is the same template right so here I can basically say demo template will be the name correct over here so I will search right demo template and I will copy this over here and paste it over here okay so oops just a second template is equal to demo template now this is done now this is how your prompt template is designed okay and I will save this in a variable called as prompt okay and now what I'm going to basically do is that whenever I give an input let's say my print prom dot format I will be using and if I give my input over here as Finance chill just see this okay Financial concept is equal to now let's say my input is income tax okay now how do llm model is going to take the entire text so if I probably just go ahead and execute it it will take something like this I want you to act as an acting financial advisor for people in an easy way explain the basics of income tax okay so this is how the message is basically going to the llm model let's say if I'm using open AI uh or any llm chains right so in in that specific way it is going to go now let's go and see that how this prompt template that we have probably created we can pass this input to our llm models right so for llm models obviously in my previous session also if you don't know I will put that particular Link in the pinned comment of this particular video uh what we'll do from Lang chain we will try to from launching Dot llms and there are a lot of llms model I've just started with open AI there is hugging phase there are different different kind of models even for chat there is different different models we'll discuss about that as we go ahead but uh prompt engineering is the first basic things that you definitely need to know so I'm going to import opening AI okay so open AI over here and then I will say from line chain dot chains whenever we use prompt template it is super important that we have to use chain for executing that particular prompt template okay so I'm going to import llm chain now you may be thinking Krish from where do you get all these things just watch this documentation right if you probably start with prompt templates over here getting started here is some examples that has been given and if you really want to run this example you have to probably use this llm chain any prompt template that you specifically want to Define okay so here it is so here is my luncheon.chins I'm going to import llm chin now the next thing is that I will initialize open Ai and let's say the temperature variable that I'm actually going to Define is 0.6 or 0.7 Whatever by default is 0.7 over here and then I'm going to basically say llm chain I'll initialize this also the first parameter is nothing but the llm model that I want to give and this is the llm model the second parameter is basically my prompt that I specifically want to give so that it can give me the output and that prompt will be nothing but the same prompt right so this is done I'll create this variable like this and let me just say that this is my chain one okay and like this I can create multiple chains which I had already shown you in my first tutorial right so once this is executed uh over here I'll be having chain one dot run right when I say run all I have to give is my input over here right and that input will basically replace this particular input over here itself right something like this so here I will say okay fine my input is that I want to know about income tax now let's go ahead and execute this now what it is going to do as soon as we write chain dot run on income tax it is going to take this prompt template inside this prompt internet wherever is my variable it is going to replace that particular word over here it will take some time based on the API key that we specifically have and again API key speed like how much cost you're paying for that you can also make a free API key but it will just give you five dollars so this is what is the output that I'm getting income tax is a tax that is paid that is paid to the government based on your income your income job investment all this information is basically available over here see so this is what it is basically giving based on this kind of input right so this is how my prompt format is it is taking this entire input and it is saying that okay with respect to the income tax I'm going to get it okay uh let's say I want to probably understand about what is GDP right so it will try to give me the answer okay about GDP over here again it will take around 8 to say 10 seconds and again it depends on the type of API that you have so GDP stands for gross domestic product total this this is there so you are able to get your own different kind of outputs right so uh this was it right and I can probably name this entire chatbot application as a financial advisor like whatever topics you specifically want you can put that okay now let me show you one more example okay and I'll give you entire materials over here now let's say I want to probably build a language translation now for this also my prompt template will be little bit different okay now see this and here I will show you an example of giving two different inputs right so language translation over here you have so here also I'm using a prompt template the template will be that in an easy way translate the following sentence into Target language so here I have to give two parameters first is my input sentence and in what target language that I really want to give ah since I am giving two parameters here also I'll use llm chain but the way of providing these two parameters will change okay just see this now this becomes my language prompt my prompt template my input variables will be again in a list sentence and target language and template will be this specific template okay now once I execute this and probably if I just slide my language underscore prompt right dot format always whenever you want to see that how the input is basically going to go you can basically write over here so sentence is my first key let's say I'm going to write how are you and I want to convert this into a target language okay this will be my target language I hope uh the spelling is correct okay so second parameter how are you and target language is equal to let's say I want to basically give as Hindi so I want to convert this sentence how are you into Hindi by using this l m chain so let me just go and see now this is how the input is going to go whenever I provide my sentence right so in an easy way translate the following sentence how are you into Hindi something like this okay now this is what we are going to use over here and again for this we will use this same chain uh we can probably create a new chain so let's say my chain two is llm uh with this particular prompt okay now since I am providing two parameters over here one is sentence and one is uh language you cannot directly call like chain one dot run or chain two dot run okay so here what I will do I will write chain two and whenever you have more than one parameters uh you have to probably give it in the form of keys right so this will be my first Keys like this sentence will be my key and in the sign set inside the sentence I can write my text something like this right so it will be like hello hello how are you okay and this is what is my sentence or let me just remove this question mark and the second parameter is nothing but it is basically my target language okay and the target language will be nothing but Hindi and you can change it you can change it to French or whatever things you specifically want okay so this is uh how you basically run it okay now if I execute it you will be able to see the output okay I'm getting one error saying that Financial concept okay Financial concept was my okay this chain two okay prompt I have to change it over here see uh this is a smaller mistake that I did because it is still referring to the previous prompt so here is my language prompt and here you have see sentence hello how are you target language is Hindi and the output text is Namaste when you get in this form of Json right now you can probably pick up whatever text you specifically want and you can give the output over there right at least it is in the form of key value bits so Namaste right and probably you can also convert this into French so French uh will come something like bonjour comment Alice was I hope it is right uh if you don't want this let's say I want to give it in kannada I don't know whether kannada is there or not uh I'm not sure let's see I hope so it should be there right hello how are you target language kannada kannada is from India itself uh hello something like that okay so all those languages can be basically be used over here okay so this is one example now similarly see I did with two parameters you do with three parameters four hundred parameters however you want to make a sentence format try to make it okay so guys now let us see one more example of a prom template and this will be quite amazing so let's consider this okay and I'm going to copy and paste one example in front of you and here you'll be able to see that I'm using one more class which is called as few short prompt template and I'll tell you why I am specifically using this but let's see this example in this example I have given a format uh this is the template format and in this template format what I'm doing is that uh I'll not say this is a template format but I'm giving some examples in this examples I have some key value pads like word is equal to happy then the opposite of this word will be nothing but antonym right so suppose let's say I'm trying to find out the opposite of this word I'm giving this format some examples like an antonym you'll be finding the word as sad so happy word opposite is sad similarly here also you have a word tall and opposite of this word tall is nothing but short so this few examples I want to give a hint to the llm model okay and considering this I am going to create my own prompt template so here I will be giving the word like this and my antonym will be looking something like this so this is my entire prompt template and before for giving this problem I also want to provide some examples over here okay to the llm model itself it can be an open AI llm model now this same template I'll be using inside this prompt template and you know that I'm giving two parameters one is word and antonym so word and antonym is here and the template is basically this example formatted template now this is perfectly fine now in order to give this examples first to my l m model we will specifically use this few short template okay and I'm just going to give you the format over here and where did I get this format it is from the documentation itself right so here I'm initializing for you short template and I'm giving first of all some of the examples right so these are the examples we want to insert into the prompt the examples is nothing but word happy antonyms sad something like this okay and what is the prompt template that we are using this is how the prompt template we are using over here the prompt template is available then the prefix I am giving give the antonym of every input that basically I'm saying the llm model to give the opposite of every input okay and the suffix will be in this format and the input variables will be input for this okay and the example separator there are some parameters you can find out all the information over here but let me show you after running this okay which is which is super important okay now let's say if I go ahead and Pro print few short prompt so few short prompt is nothing but the same thing okay and here I want to probably write dot format and the input parameter that I'm giving to this is nothing but this input right so let's say if I give this input is equal to and let's say I'm going in the word big so this is how the entire uh uh the input will be given to the llm models so given the antonym of every input what is equal to happy with some examples this is my word now I'll be waiting for this specific answer okay now the llm models will be able to understand in a better way okay now directly to run this all I have to do create a chain okay create a chain over here and I am going to give this few short term prompt over here so the same few short prompt over here and the chain will just run with input is equal to Big okay I can either run like this or I can also say chain dot run see this if I write chain dot run and I'll give my input as big right so I will be getting the output over here you'll be see big as small so if I remove dot run and probably keep the previous format I will be able to give the entire things like what is my input is equal to Big text is equal to small so in short I'm first of all this Pro a few short prompt is basically giving the entire examples to my in a model and then finally I am able to get the output so these are some of the examples with respect to this uh here I've also explained a few short templates a short prompt templates so that you can also give an example so quick summary about the prompt engineering guys again a very good thing probably you want to create own chatbot models on a models at that time you want to give your own input format you can definitely do it with the prompt template itself and I've heard like many places now prompt engineering job is also on fire so that basically means a lot of openings where people get highly paid but at the end of the day my work is to basically teach you regarding that and I have done that okay so yes this was it for my side I will see you all in the next video have a great day thank you take care bye

Channel: Krish Naik

Views: 53,718

Keywords: yt:cc=on, prompt engineering, langchain, large language models, LLMs, AI, artificial intelligence, natural language processing, NLP, text generation, creative writing, coding, programming, web development, machine learning, ML, deep learning, DL, cot, react, chain of thoughts, reactive prompt selection, poetry, fiction, krish naik langchain tutorials

Id: t2bSApmPzU4

Length: 17min 19sec (1039 seconds)

Published: Thu Jun 15 2023