Getting the prompt right is hard especially if you want the prompt to deliver consistent results at scale but what if you can control the prompt to get specific output structure and get a general image and the charts with just one single prompt there are few Advanced proper engineer Frameworks that I found extremely useful but not enough people using it and that's what I want to talk about today the first one I want to talk About is called guidance it is an open source framework initially introduced by Microsoft and got more than 12 000 stars on GitHub fundamentally it is a framework that allow you to program the prompt to get specific output it gives you very specific control of how the final output structure should look like and also allow you to construct The Prompt with for each Loop Define list of candidate answers and even insert if conditions inside your prompts so this Allows you to configure the final output very easily to get started you can open Visual Studio code and then we can create a jupyter notebook that we install ipy and B so firstly we will install the guide lens and open AI package and we will import the guidance and then to Guidance the large language model with openai text DaVinci you can also use GPD 3.5 gp4 or even open source model like llama as well but they do have specific syntax you need to follow And the first thing we will try is Define a specific output structure you want so you can create something like prompt template like how you do in launching by creating variable with guidance and inside here you will Define The Prompt template where I would Define one variable with this bubble curly bracket and also another double curly bracket with special term gen the part that you actually want large model to creating so the final output would look Something like this when Steve Jobs said without guidance AI would do more harm than good with this it allow you to get a specific structure output and if you want you can even change multiple output together so I can add another one counter argument and let me run this so you can see here the large language model actually generates this two parts and without guidance framework it's actually possible to do manually but it will just require a a lot of manual work And apart from defining the structure you can also restrict the actual output the large energy model going to generate for example I can ask large knowledge model to choose one of the predefined answers instead of creating their own so I can define a list of options here and then create another guidance is the following sentence offensive where I will give an example and ask it to select an answer from the three options I give here I'll give you example is Pretty rude your taco tastes like and try to run this so it will follow that specific structure and give me the answer is yes and here I didn't give it any extra prompt about what the answer should be just follow the list of predefined answers I give and if I change this to be something more polite like your tacos taste can be improved and try it again then it will answer no so this is a really powerful way that if we want to restrict down the answers the Large language model actually generates in use case like writing email or customer response where you still want to use the reasoning part of the large language model but you already got best practice and the other part I want to talk about is that you can also set up Advanced logic like a if condition for example in the same use case where we asked large language model to classify if the user response is root we can actually create a workflow where if the Answer is rude then it will trigger specific typo response if the answer is not root then the system will generate a response like normal the way we will do that is we'll still use a slap and give the response name root and this basically means we will have a variable called root that we can use in other parts of the prop and then we can create a if condition if root is yes which is from the predefined condition we give then a system will say please be polite But and if root is no then a system will generate answer so let's try this okay with this user response your toggle tests are shared the large large model detail it is actually root so it triggers this workflow where a system will say please be polite but if I change to something like your taco tastes too salty it'll be showing the answer is not rude then it actually generate a proper response so this is a way for you to achieve if condition Larger in your final output and these are the value costs usage as well for example I probably don't want this part in the middle to be showing in the final output because it's kind of internal logic so I can actually hide this from the final output by creating block to wrap this part with attribution hidden equal to true and if I run this again then the part in the middle will be hidden so this is how you can achieve if condition and I think if condition is Probably one of the most powerful use case for guidance those are just a few functions that guidance have and they are actually more things that you can learn from their documentations but with those basic building blocks you can already create very Advanced props for example for the use case when I want light language model to generate emails one problems I always face with is that sometimes I do want to use the reasoning power of large Range model but I want Response to be exactly like the best practice that I come up with and I don't want any creativities around them and on the other side I also want to create some if conditions there if this client is very important then book a meeting right away but if it's just general in priority then just response like normal so to achieve their use case I will Define a list of predefined Priority First from low to high and I'll create one blocks at the beginning to let large Language model give a priorities and then I will ask large language model to generate the email response and in the end I will hide those two logic blocks and showing the email response is generated and if the priority is high priority then I want to insert this specific message to schedule call with customers right away with my calendar link so that's why I don't want it to be created because if you change the link it won't work so let's try it assume a Customer complaint come to my inbox your server is so terrible that I want to refund this is kind of high stake situation that I wanted to schedule call right away so you can see here it actually generate a message also insert the predefined message with my calendar link but if I change to be something more soft like what features does webflow have but Wix don't have then this will show a different message and won't trigger this specific calendar Link and on the other side I can also use guidance framework to let large knowledge model generate real-time hard for me with service like quick chart and if you don't know what a quick chart is it's a open API where it allowed me to generate charts in real time by passing on the chart data in a URL if I can get a URL looks something like this then it will generate this chart on the Fly which means if we can get large energy model to turn a natural language query Into a data structure like this then we can generate charts in real time and here we will use another function from guidance which is ability to trigger custom functions inside the problem so the way we will do that is I will give the prompt a few short examples so that it will learn if the input is this it should generate Json data like this and once it generates Json data I will use this function pause chart link to put together a URL and displaying markdown So the way I would do that is I would Define this function that will pass on the Json file into a URL linking markdown and then I will Define a few short examples here and then I'll create another Guidance with this prompt you are a world-class data analyst you will generate chart output based on the natural language which in here I have this 4 inch wrapper and I will run this q a pair from the predefined list and once they finish the free of sharp Prompts I will insert the actual user query and I'll see large language model to generate chart data and all this logic will be hidden from the final output because I wrap under this block and the final output will be hello here is the charter you want and the result will be calling the customer function I defined above with the variable chart the large language model created which is Json data and once I did that I can try to use this function create a pie Chart showing the population of the word by continent it will generate this response and it is in markdown format so if you paste this in a markdown preview you will see this is results a population breakdown by continent chart that generated in real time and if you want I also include a streamlined app where you can visualize and test those three dots better and with the same shred of salt you can also use large language model to generate image Straight away with service like pollinations and if you don't know what pollination is it's also another open API they allow you to create an AI image on the Fly for example I can do image.pollination.ai prompt a cute girl and this will generate this image right away and this allows us to build a use case like if I give a story idea it can generate a whole story as well as a proper illustration so I can do something similar where I will have a Logic block to ask large language model to generate story based on my story idea and then have another block to generate the image linking markdown based on the story and in the end I'll put them together and if I try this again it will generate a proper story from a single prompt and also generate proper illustration as well so this is a guidance framework I think it's very powerful even though it is still a bit buggy and the lack of documentation but It is quite powerful once I get it I have attached all those example codes I show you here in the description below so feel free to try it out even though guidance is a very powerful framework but at the end of the day it will be still the iterative process you will still need to spend a top time fine tuning and iterating your prompts to get the best results but there are a few other community and open source projects that I think can really speed up the Process here one of them is flow GPT so flow GPT is one of the biggest prompt library and probably one of the biggest prompt engineering community as well I always go up to flow GPT every time when I try to build some specific prompts because it's a wide range of collections from marketings programming so for example if I want to create a prompt for SEO blocks I can just come here see what are the prompts that Community has voted most and I can click on that take a look About the problems that other people create so this provides me a much better starting point and I can also get a preview about what the readouts will be this is where I learned a lot of tactics here for example I actually learned about permissions from The Prompt here because someone actually built a full text-based adventure with just one single prop which is super impressive so I definitely recommend to use this as a prompt Discovery tool at the rare Beginning and on the outside there's also another project called GPT prompt engineer and the concept is pretty simple it basically asks GPD to generate prompt and also use GPT to evaluate and test the quality of prompts so it refers to use gpd4 to generate a set of prompts based on the goal you give it for example I can use GPT prompt engineer to generate prompt for writing SEO blog posts and it will use gpd4 to generate 4 or 10 different prompts and then it will Start using gbd4 as evaluation machine to do the testing it was starting doing 20 or 30 rounds of testing across prompts and figure out which one is the best to be honest from my experience the prompts generated by GPD is actually not as good as the one set you will come up by yourself so I won't rely purely on gpe to generate bombs autonomously however the part I found is most value is actually this evaluation framework because normally I will come up with two Or three different variations between the prompts and I want to figure out which one gonna performs best at scale they're actually someone built a front end based on this GPD prompt engineer called prompts Royal where you can basically get GPT to generate a prompt and do the testing and once it finished it will show you the evaluation results between different prompts and in the end give you a score so even though I don't really like the default prompts that GPT Generated but I often use this as a way to evaluate different prompts I generated so I can manually paste in two or three different prompts that I created and use this platform to evaluate which one is better so those are a few learnings I want to share with you about problem engineer if you know more interesting tactics and Frameworks please comment below and let me know if you enjoyed this content please consider give me subscribe I'll continue posting Interesting AI projects that I'm building thank you and I see you next time