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

Hello , all my name is Krishna and welcome to my youtube channel .

So guys in this video , we are going to probably discuss about prompt engineering using chain .

Now , prompt engineering can be super important if you are probably working with L L M 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 LA and I also showed you that how we can actually create a simple search application just like a chatbot using Lain .

Now let's go ahead and let's discuss about prompt engineering .

So um in prompts uh engineering , we specifically design the prompts itself , right ?

So if I talk about LAIN , 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 referred to the input to the model .

This input is often constructed from multiple components .

So here in LA , uh you will be able to create your own prompts using this prompt template , right ?

So it is responsible for construction of the inputs .

Lain 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 let or there also the prompt template can be used from the LA 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 A I key , the API key , I will talk about it later on because uh let's say if you're probably creating a chatbot and you want to also use Google search and that we can actually use the Google search API .

So for that purpose , I would 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 .

OK .

As discussed earlier , we are going to specifically use prompt prompt template from the LA chain itself .

So now let's go ahead and import from LA chain .

OK ?

And many people who are asking what are the prerequisites 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 LAIN documentation with respect to the syntax and the parameters that we are going to use .

OK .

So from LA we are basically going to import the prompt template .

OK ?

And uh 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 prompt template over ac 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 always write this in cards .

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 the input or two parameters , provide two parameters as the 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'm basically using for designing my input is the same template , right ?

So here I can basically say uh demo template will be the name correct over here .

So I will search demo template and I will copy this over here and paste it over here .

OK ?

So oops just a second template is called to demo template .

Now this is done .

Now this is how your prompt template is designed .

OK ?

And I will save this in a variable called as prompt .

OK ?

And now what I'm going to basically do is that whenever I give an input , let's say my print prompt dot format I will be using .

And if I give my input over here as financial , just see this .

OK ?

Financial concept is equal to .

Now let's say my input is income tax .

OK ?

Now how the L L M 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 .

OK ?

So this is how the message is basically going to the L L M model .

Let's say if I'm using open A I uh or any L L M 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 L L M models , right ?

So for L L M models , obviously in my previous session , also , if you don't know , I will put that particular link in the pin comment of this particular video .

Uh What we'll do from LA chain , we will try to from la chain dot L L MS and there are a lot of L L MS model .

I've just started with open A I .

There is hugging phase , there are different , different kind of models .

Even for chat , there is different , different models we'll discuss about it 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 open A I OK .

So open A I 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 .

OK ?

So I'm going to import L L M chain .

Now you may be thinking Chris from where do you get all these things ?

Just was this documentation , right ?

If you probably start with prompt templates over here getting started .

Here are some examples that has been given .

And if you really want to run this example , you have to probably use this uh L L M chain , any prompt template that you specifically want to define .

OK .

So here it is .

So here is my line chain dot chains .

I'm going to import L L M chain .

Now , the next thing is that I will initialize open A I 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 L M chain , I'll initialize this .

Also the first parameter is nothing but the L L M model that I want to give .

And this is the L L M model .

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 I'll create this variable like this .

And let me just say that this is my chain one .

OK ?

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 , OK , fine .

Uh 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 change or run on income tax , it is going to take this prompt template inside this prompt template .

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 , uh API key speed uh like how much cost you're paying for that , you can also make a free API key , but it will just give you $5 .

So this is what is the output that I'm getting income tax is a tax that is paid um that is paid to the government uh 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 OK .

With respect to the income tax , I'm going to get it .

Ok .

Uh let's say I want to probably understand about what is GDP , right ?

So it will try to give me the answer , ok , about GDP .

Over here again , it will take around 8 to 10 seconds and again , it depends on the type of API that you have .

So GDP stands for gross domestic product total .

This , this , this is that 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 chat board application as a financial advisor , like whatever topics you specifically want to can put at .

OK .

Now let me show you one more example .

OK ?

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 a little bit different .

OK ?

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 what target language that I really want to give .

Uh since I'm giving two parameters here , also I'll use a chain .

But the way of providing these two parameters will change .

OK ?

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 .

OK ?

Now once I execute this and probably if I just like 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 .

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 .

OK .

This will be my target language .

I hope uh the spelling is correct .

OK .

So second parameter , how are you ?

And target language is called to , let's say I want to basically give us Hindi .

So I want to convert this sentence .

How are you into Hindi by using this L N M chain ?

So let me just go and see .

Now this is how the input is going to go when I , I provide my sentence , right ?

So in an easy way to translate the following sentence , how are you into Hindi ?

Something like this ?

OK .

Now this is what we are going to use over here .

And again , for this , uh we will use the same uh chain , uh we can probably create a new chain .

So let's say my chain two is L L M uh with this particular prompt .

OK .

Now , since I'm providing two parameters here one is sentence and one is uh language you cannot directly call like chain one dot run or chain two dot run .

OK .

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 said inside this sentence , I can write my text something like this , right ?

So it will be like hello , hello , how are you ?

OK .

And uh 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 .

OK ?

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 .

OK ?

So this is uh how you basically run it .

OK ?

Now if I execute it , you'll be able to see the output .

OK ?

I'm getting an error saying that financial concept , OK .

Financial concept was my OK ?

This chain too .

OK ?

Prompt .

I have to change it away .

I 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 when you get in this form of Jason right now , you can probably pick up whatever text you specifically want and you can give the output over there , right ?

At at least it is in the form of key value pairs .

So , right ?

And probably you can also convert this into French .

So French uh will come something like or comment Alice was I hope it is right .

Uh If you don't want this , let's say I want to give it in Canada , I don't know whether Canada 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 ?

Canada , Canada is from India itself .

Uh Hello , something like that .

OK ?

So all those languages can be basically be used over here .

OK ?

So this is one example .

Now similarly see I did with two parameters , you do with three parameters , 400 parameters .

However , you want to make a sentence format , try to make it .

OK ?

So guys , now let us see one more example of a prompt template and this will be quite amazing .

So let's consider this .

OK ?

And I'm going to copy and paste one example in front of you .

And uh here you'll be able to see that I'm using one more class , which is called a few short prompt template .

And I'll tell you why I'm specifically using this .

But let's see this example .

In this example , I've given a format uh this is a 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 these examples .

I have some key value pads .

Like what is it called 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 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 the opposite of this word tall is nothing but shot .

So this few examples , I want to give a hint to the L L M model .

OK ?

And considering this , I'm going to create my own prompt template .

So , 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 giving this prompt template , I also want to provide some examples over here .

OK ?

To the L L M model itself , it can be an open A I L L M 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 format template .

Now this is perfectly fine .

Now , in order to give these examples first to my L N M model , we will specifically use this few short template .

OK ?

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 free 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 antonym , sad , something like this .

OK ?

And what is the uh 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'm giving , give the antonym of every input that basically I'm saying the model to give the opposite of every input .

OK ?

And the suffix will be in this format and the input variable will be input for this .

OK ?

And the example separated , there's some parameters you can find out all the information over here .

But let me show you after running this OK ?

Which is , which is super important .

OK ?

Now let's say if I go ahead and print few short prompt , so few short prompt is nothing but the same thing .

OK ?

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 with the word big .

So this is how the entire uh uh the input will be given to the L N M models .

So given the antonym of every input , what is going to happen with some examples ?

This is my word .

Now , I'll be waiting for this specific answer .

OK .

Now the L L M models will be able to understand in a better way .

OK .

Now directly to run this all I have to do create a chain , OK ?

Create a chain over here .

And I am going to give this few short prompt over here .

So the same few short prompt over here and the chain will just run with input is called too big .

OK ?

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 do and probably keep the previous format , I will be able to give the entire things like what is my input is to big text is equal to small .

So in short , I'm first of all , this pro few short prompt is basically giving the entire examples to my L N M model .

And then finally , I'm able to get the output .

So these are some of the examples with respect to this .

Uh uh Here , I also explained you for short templates , uh short prompt template 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 your own chatbot models on 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 .

Ok .

So yes , this was it from my side .

I will see you all in the next video .

Have a great day .