The Logical Problem Solving Strategy: A Journey to Empower LLMs using Prompt Engineering

As I embarked on this journey, I found myself at the cusp of a new era in artificial intelligence. The Logical problem solving for our LLMs using prompt engineering was an exciting prospect that promised to revolutionize the way we interact with machines. With a newfound enthusiasm, I set out to empower my LLM by crafting a customized setup that would take it from its current limitations to unprecedented heights.

I started by removing brackets and embracing the excitement of discovery. The moment of truth had arrived, and I was eager to see what our system would yield. As I ran the test, I couldn't help but feel a sense of trepidation mixed with anticipation. Would my LLM be able to rise to the challenge? Only time would tell.

The first hurdle we encountered was problem decomposition prompts. I was pleased to find that this aspect of prompt engineering proved to be a valuable tool in breaking down complex problems into manageable components. If the problem at hand was to plan a trip from City A to CDB, for instance, the decomposition prompts would require us to identify all possible ways from City A to City B, list pros and cons, consider the fastest route, and more. This exercise allowed me to appreciate the nuances of prompt engineering and its potential to empower LLMs.

Another crucial aspect of our setup was hypothesis generation prompts. I found it fascinating that this type of prompt could generate five possible solutions for a given problem. This feature not only showcased the creativity of LLMs but also highlighted the importance of diverse perspectives in solving complex problems.

We also delved into evidence evaluation prompts, which allowed us to assess the probability of different scenarios and make informed decisions. Decision making action plans contingency review and iteration were other critical components that rounded out our setup. Each of these elements played a vital role in shaping the capabilities of my LLM.

As I prepared for the final step, I chose a five-step prompt sequence inspired by The Logical problem solving strategy. This sequence would serve as the backbone of our system, guiding it through the process of problem-solving with precision and accuracy. With this setup in place, we were ready to test our LLM against a real-world benchmark problem.

I opted for a classic example: measuring 6 liters using only two jugs, one of which is 12 liters and the other is 6 liters. This problem had stumped many solvers before me, but I was determined to crack it. By describing the problem in detail, I hoped to elicit a response from my LLM that would showcase its capabilities.

The first attempt yielded an unsatisfactory result, with the LLM struggling to grasp the simplicity of the problem. However, upon regenerating the prompt, we received a revised answer that demonstrated a deeper understanding of the issue. The LLM had grasped the concept of filling one jug to achieve the desired outcome, and I couldn't help but feel a sense of pride at having empowered my tool.

As I reflected on our journey, I realized that prompt engineering was not just about crafting clever questions; it was about creating an environment that fostered growth and understanding. Our experiment had shown that even with the best setup, LLMs were not infallible and could sometimes struggle with seemingly simple problems. However, by pushing their limits and testing them against diverse prompts, we could coax out innovative solutions and unlock new levels of performance.

Throughout our journey, I discovered the value of exploring different papers and techniques to refine prompt engineering. By doing so, I gained a deeper appreciation for the complexity of LLMs and the importance of adaptability in problem-solving. As I wrapped up this experiment, I couldn't help but feel that we had only scratched the surface of what was possible with prompt engineering.

In conclusion, our journey had been one of discovery, growth, and empowerment. By embracing the power of prompt engineering, we had taken a bold step towards unlocking the full potential of LLMs. As I looked to the future, I knew that this experiment would serve as a foundation for further research and innovation, inspiring others to explore the vast possibilities of prompt engineering.

As I concluded my article, I couldn't help but feel a sense of excitement about the prospect of exploring new prompts and techniques. The journey had shown me that with creativity, persistence, and a willingness to learn, even the most seemingly insurmountable problems could be solved. And as I delved into my next project, I knew that I would continue to push the boundaries of what was possible with LLMs, armed with the knowledge and inspiration gained from this incredible journey.

Role Persona: Professor Alpha

As we embarked on our experiment, I assumed the role of Professor Alpha, a renowned academic specializing in problems solving strategies. With a deep understanding of the subject matter, I was eager to guide my LLM through the process of problem-solving with precision and accuracy.

Your Turn!

With this setup in place, it's time for you to take the reins! You are now the master of prompt engineering, armed with the knowledge and inspiration gained from our experiment. It's your turn to test your LLM against a real-world benchmark problem and unlock its full potential.

What prompts will you choose? What problems will you tackle? The world is your playground, and the possibilities are endless!

"WEBVTTKind: captionsLanguage: enin today's video I want to show you how I've been doing research to create new prompt sequences for chat GPT or other llms this is a low barrier way to find interesting approaches to all kind of problems you want to solve with large language models so let's just get going let me just show you my workflow here I usually just start off to find some research paper on Arc except org or on the web then I take the research paper I font usually they are in PDF I go to the chat GPT plugins and I use a plugin I'm going to show you there to summarize them and get the information I want then I save these summaries to a text file I can do multiple papers and save all this into a summary in the same text file then I take these summaries feed them back to chat GPT and I try to create a framework from them so that's basically write a prompt sequence framework from the papers I'm going to show you the prompts then I just save the framework and I go back to chat GPT again to test it and see if it can produce some good results and that is basically it and I'm gonna show you that now the best resource I know for finding these papers are arcsave.org I hope that's how you pronounce it but anyway you can just go up in the field here and search so I usually just search for prompting large language model and then you get a lot of these papers coming up so then I just skim through them see if I find something that sounds interesting strategic reasoning with language model okay that sounds interesting now just open that scroll further down prompt based tuning okay so then I just look here until I find something short answer grading using one shot prompting code prompting a neural symbolic method and open that llms can understand encrypted prompts okay so there's a ton of papers coming out lately so there's a lot of pick from so I usually just scroll a bit through them ah this looks quite an interesting so what I do next I just copy the URL here and head over to chat TPT and over here what I want to do is go into the plugins section so if you don't have it you can go here to settings you can go to Beta and highlight these two and the two plugins I use is link reader and ask your PDF you can find them in the plugin store so basically the prompt I'm gonna use now is can you please write an in-depth summary of the following PDF okay so I'll just paste in the URL to the PDF and click submit and here you can see the plugins is using ask your PDF and writing a summary when we have the summary I follow up with this prompt write a detail step-by-step instruction on how the framework works okay so you can see this framework is designed to enhance strategic reasoning capabilities of llm making them more reliable and flexible in diverse strategic diverse strategic scenarios so we have some points here guiding decision making prompting strategies Matrix Games negotiation games for me leaves Factory strategic demonstration prompts okay so the next step I usually go then is could you give me some example of prompts chain prompts and other prompt engineering techniques from the framework okay so these fronts are designed to guide decision making process of the language model okay so let's take a look at them we have search prompts value assignment prompts belief tracking prompts Chain of Thought racing prompts are like those Cascade prompts demonstration prompts so that is basically what we need from the first paper now I want to select one more paper to try to integrate those two I rarely go more than two papers but we will see let me grab our next paper and that is going to be a really interesting paper just so that's called Oola GPT empowering llms with human-like problem solving abilities so I'm just gonna grab this URL spin it through the same as we just did and then we're going to take a look at how I combine those two okay so now I have the information I want you can see from the last paper we have problems with generating questions prompts for thinking templates prone for plant thinking prompts for step thinking prompts for critical thinking okay so that sounds very interesting the next step now is gonna be to use this to come up with some new interesting creative prompting sequences but before we move on to that I'm proud to say I have partnered up with Nvidia on this channel and I just wanted to show off some cool stuff they presented this week ever since I started playing around with gpt3 I guess it's almost three years ago now I always thinking like how could this impact the gaming industry and I think I got my answer a few days ago now when Nvidia released this video created by Nvidia using the latest RTX rendering Technologies Nvidia collaborated with convey and Nvidia Inception partner to Showcase how developers will soon be able to use Nvidia Riva Nemo and audio to face for ai-powered speech conversation and animation these models were integrated into the convey Services platform and fed into Unreal Engine 5 and metahuman to bring Jin to life please take a look hey Jen how are you unfortunately not so good how come I'm worried about the crime around here it's gotten bad lately my Ramen Shop got caught in the crossfire can I help if you want to do something about this I have heard rumors that the powerful crime Lord Kumon ayoki is causing all sorts of Chaos in the city he may be the root of this violence I'll talk to him where can I find him I have heard he hangs out in the underground fight clubs on the city's East Side try there okay I'll go be careful Kai wow that is so cool right so let's take a look at how this works Nvidia Ace for games combines three amazing tools Nvidia Nemo and video Riva and Nvidia Omniverse audio to face Nemo creates customable language models for specific characters backstories personalities traits and stuff while Riva turns speech into text and text back into speech so this really enables real-time conversations Omniverse audio to face then generates a lifelike facial animations from audio integrating perfectly with Unreal Engine 5. developers can make these characters behave just the way they want by using behavioral cloning techniques and even reinforcement learning from Human feedback once everything lines up they apply the Nemo guard rails toolkit to make sure characters stay accurate on topic and of course secure if you want to learn more about this amazing Tech check out the links in the description below to support me a bit and our partner and video and get ready for the future of gaming okay so what I do now is I open up a new window with the default gpt4 so I'm not in the plugins not now then I just use this prompt ignore all previous instructions you are an AI llm and machine learning expert your task is to read and understand given information Be Inspired about that information that come up with new ideas that could have better results that the user would need to research acknowledge all of this by understanding understood and stay idle okay so we get understood then we can move on with our next prompt then I want to take all the information I got from the papers and I just want to copy all of this it seems a bit much but I think it's fine we're gonna find that out then I'm just gonna head back over here and paste it in so you can see it now we have all the information on top here and we have the following prompt let's think about this in a step-by-step way and brainstorm a new way to Super enhance and here I want to set in set in the problem or the kind of skills I want to try to give to the llm this could be like writing a story doing some kind of financial stuff I'm gonna do logical problem solving skills for an llm with very smart use of prompting chain prompting prompters name use word system should not replicate the papers in the context but find a new way to empower The Logical problem solving for our llm using prompt engineering Chain of Thought reasoning and other super smartphone sequences system prompts and prompting techniques so that is basically my setup I'm going to take off these brackets and then I'm just gonna run it that is the exciting moment to see what we get back this looks quite promising let's take a quick look here so we got back uh the first we have is problem decomposition prompts I like this one so if the problem is plan a trip from City a to cdb the decomposition prompts would be identify all possible ways from City a to City B for each possible way list pros and cons consider the fastest yeah I like this hypothesis generation prompts generate five possible solutions for a given problem I like that because we want different solutions it can reconsider evidence evaluation prompts probability assignments yes this is decision making action plans contingency review and iteration yeah okay so we have one step left before we hopefully have our system and that is gonna be great could you write here I just chose five step prompt sequence I can use in chat Jeep T from The Logical problem solving strategy about to solve my problem okay so let's just click submit and then it wants me to describe my problem in detail and I'm gonna go ahead and do that because I have a benchmark problem I always try to test and that is I have a 12 liter jug and a 6 liter jug I want to measure 6 liters how do I do it okay so now we have our step-by-step guide so now it's just time to test it so I'm gonna copy this and then we're gonna open a new window and test it I almost forgot but I also sometimes just go can you also create a system prompt role persona for chat GPT to act as okay so this is the prompt we ended up with you are Professor Alpha a renowned academic with a specialization in problems solving strategies uh no no so yeah I'm not going to read all of that but I'm definitely going to grab it when we are going to try to solve our problem okay then we are ready so we have all our prompts lined up here so I'm just gonna open up a new window and let's get testing all right so I'm just gonna paste in our first prompt here okay so we start off very bad here so you have a 12 meter yoga and a six liter jog the 12 liter jug is initially empty so it's the six liter joke your aim is to measure x-axis 6 liters it's horrible summary of The End type 12 with York 6 liters that's the wrong answer so we're just gonna move on let's grab our next prompt and see what happens no still nothing still just struggling here so it changed the states of jig in certain ways so filler jug empty our jug pour one from jug into another okay that's the three states that is correct uh but what we want to find of course is the reasoning that it kind of understands that you can just fill one jug but this is a hard problem I haven't encountered many times so let's move on to step three prompt three did not solve our problem it just keeps on thinking that it has to use two jokes even though I have not said anything about that so we have to go to step four I guess nope keeps just tagging along on this side so it's not gonna be right this time let's just slap in the last prompt here and see what we get the conclusion here is that this prompt did not work for our Benchmark problem but of course this video was not about exactly how this result turned out it's more about how you can do some research with some different papers and try to come up with new prompting sequences skills techniques color whatever you want to try to solve a different problem I just found this very interesting something I'd just like to do and when I do this Benchmark I always like to do why didn't you just fill the six liter jug to get 6 liters I don't know what happened I just got a network error so I guess he didn't want to answer let's try to regenerate so it goes you're absolutely correct I appreciate your simplification of the problem my early responses were based on the premise of manipulating both jokes to reach the desired outcome it always says that thank you for your observation and I apologize for my confusion okay so that was better but anyway thank you for tuning in I hope maybe this is something you can be interested in I know it's a bit Niche but I had a lot of fun with testing out this and you can really learn something if you want to dive deeper into prompts different llm models this is not just applied to chat gbt you can use it for other things too but again thank you for tuning in check out some of my other videos on the screen here and I'll see you in the next onein today's video I want to show you how I've been doing research to create new prompt sequences for chat GPT or other llms this is a low barrier way to find interesting approaches to all kind of problems you want to solve with large language models so let's just get going let me just show you my workflow here I usually just start off to find some research paper on Arc except org or on the web then I take the research paper I font usually they are in PDF I go to the chat GPT plugins and I use a plugin I'm going to show you there to summarize them and get the information I want then I save these summaries to a text file I can do multiple papers and save all this into a summary in the same text file then I take these summaries feed them back to chat GPT and I try to create a framework from them so that's basically write a prompt sequence framework from the papers I'm going to show you the prompts then I just save the framework and I go back to chat GPT again to test it and see if it can produce some good results and that is basically it and I'm gonna show you that now the best resource I know for finding these papers are arcsave.org I hope that's how you pronounce it but anyway you can just go up in the field here and search so I usually just search for prompting large language model and then you get a lot of these papers coming up so then I just skim through them see if I find something that sounds interesting strategic reasoning with language model okay that sounds interesting now just open that scroll further down prompt based tuning okay so then I just look here until I find something short answer grading using one shot prompting code prompting a neural symbolic method and open that llms can understand encrypted prompts okay so there's a ton of papers coming out lately so there's a lot of pick from so I usually just scroll a bit through them ah this looks quite an interesting so what I do next I just copy the URL here and head over to chat TPT and over here what I want to do is go into the plugins section so if you don't have it you can go here to settings you can go to Beta and highlight these two and the two plugins I use is link reader and ask your PDF you can find them in the plugin store so basically the prompt I'm gonna use now is can you please write an in-depth summary of the following PDF okay so I'll just paste in the URL to the PDF and click submit and here you can see the plugins is using ask your PDF and writing a summary when we have the summary I follow up with this prompt write a detail step-by-step instruction on how the framework works okay so you can see this framework is designed to enhance strategic reasoning capabilities of llm making them more reliable and flexible in diverse strategic diverse strategic scenarios so we have some points here guiding decision making prompting strategies Matrix Games negotiation games for me leaves Factory strategic demonstration prompts okay so the next step I usually go then is could you give me some example of prompts chain prompts and other prompt engineering techniques from the framework okay so these fronts are designed to guide decision making process of the language model okay so let's take a look at them we have search prompts value assignment prompts belief tracking prompts Chain of Thought racing prompts are like those Cascade prompts demonstration prompts so that is basically what we need from the first paper now I want to select one more paper to try to integrate those two I rarely go more than two papers but we will see let me grab our next paper and that is going to be a really interesting paper just so that's called Oola GPT empowering llms with human-like problem solving abilities so I'm just gonna grab this URL spin it through the same as we just did and then we're going to take a look at how I combine those two okay so now I have the information I want you can see from the last paper we have problems with generating questions prompts for thinking templates prone for plant thinking prompts for step thinking prompts for critical thinking okay so that sounds very interesting the next step now is gonna be to use this to come up with some new interesting creative prompting sequences but before we move on to that I'm proud to say I have partnered up with Nvidia on this channel and I just wanted to show off some cool stuff they presented this week ever since I started playing around with gpt3 I guess it's almost three years ago now I always thinking like how could this impact the gaming industry and I think I got my answer a few days ago now when Nvidia released this video created by Nvidia using the latest RTX rendering Technologies Nvidia collaborated with convey and Nvidia Inception partner to Showcase how developers will soon be able to use Nvidia Riva Nemo and audio to face for ai-powered speech conversation and animation these models were integrated into the convey Services platform and fed into Unreal Engine 5 and metahuman to bring Jin to life please take a look hey Jen how are you unfortunately not so good how come I'm worried about the crime around here it's gotten bad lately my Ramen Shop got caught in the crossfire can I help if you want to do something about this I have heard rumors that the powerful crime Lord Kumon ayoki is causing all sorts of Chaos in the city he may be the root of this violence I'll talk to him where can I find him I have heard he hangs out in the underground fight clubs on the city's East Side try there okay I'll go be careful Kai wow that is so cool right so let's take a look at how this works Nvidia Ace for games combines three amazing tools Nvidia Nemo and video Riva and Nvidia Omniverse audio to face Nemo creates customable language models for specific characters backstories personalities traits and stuff while Riva turns speech into text and text back into speech so this really enables real-time conversations Omniverse audio to face then generates a lifelike facial animations from audio integrating perfectly with Unreal Engine 5. developers can make these characters behave just the way they want by using behavioral cloning techniques and even reinforcement learning from Human feedback once everything lines up they apply the Nemo guard rails toolkit to make sure characters stay accurate on topic and of course secure if you want to learn more about this amazing Tech check out the links in the description below to support me a bit and our partner and video and get ready for the future of gaming okay so what I do now is I open up a new window with the default gpt4 so I'm not in the plugins not now then I just use this prompt ignore all previous instructions you are an AI llm and machine learning expert your task is to read and understand given information Be Inspired about that information that come up with new ideas that could have better results that the user would need to research acknowledge all of this by understanding understood and stay idle okay so we get understood then we can move on with our next prompt then I want to take all the information I got from the papers and I just want to copy all of this it seems a bit much but I think it's fine we're gonna find that out then I'm just gonna head back over here and paste it in so you can see it now we have all the information on top here and we have the following prompt let's think about this in a step-by-step way and brainstorm a new way to Super enhance and here I want to set in set in the problem or the kind of skills I want to try to give to the llm this could be like writing a story doing some kind of financial stuff I'm gonna do logical problem solving skills for an llm with very smart use of prompting chain prompting prompters name use word system should not replicate the papers in the context but find a new way to empower The Logical problem solving for our llm using prompt engineering Chain of Thought reasoning and other super smartphone sequences system prompts and prompting techniques so that is basically my setup I'm going to take off these brackets and then I'm just gonna run it that is the exciting moment to see what we get back this looks quite promising let's take a quick look here so we got back uh the first we have is problem decomposition prompts I like this one so if the problem is plan a trip from City a to cdb the decomposition prompts would be identify all possible ways from City a to City B for each possible way list pros and cons consider the fastest yeah I like this hypothesis generation prompts generate five possible solutions for a given problem I like that because we want different solutions it can reconsider evidence evaluation prompts probability assignments yes this is decision making action plans contingency review and iteration yeah okay so we have one step left before we hopefully have our system and that is gonna be great could you write here I just chose five step prompt sequence I can use in chat Jeep T from The Logical problem solving strategy about to solve my problem okay so let's just click submit and then it wants me to describe my problem in detail and I'm gonna go ahead and do that because I have a benchmark problem I always try to test and that is I have a 12 liter jug and a 6 liter jug I want to measure 6 liters how do I do it okay so now we have our step-by-step guide so now it's just time to test it so I'm gonna copy this and then we're gonna open a new window and test it I almost forgot but I also sometimes just go can you also create a system prompt role persona for chat GPT to act as okay so this is the prompt we ended up with you are Professor Alpha a renowned academic with a specialization in problems solving strategies uh no no so yeah I'm not going to read all of that but I'm definitely going to grab it when we are going to try to solve our problem okay then we are ready so we have all our prompts lined up here so I'm just gonna open up a new window and let's get testing all right so I'm just gonna paste in our first prompt here okay so we start off very bad here so you have a 12 meter yoga and a six liter jog the 12 liter jug is initially empty so it's the six liter joke your aim is to measure x-axis 6 liters it's horrible summary of The End type 12 with York 6 liters that's the wrong answer so we're just gonna move on let's grab our next prompt and see what happens no still nothing still just struggling here so it changed the states of jig in certain ways so filler jug empty our jug pour one from jug into another okay that's the three states that is correct uh but what we want to find of course is the reasoning that it kind of understands that you can just fill one jug but this is a hard problem I haven't encountered many times so let's move on to step three prompt three did not solve our problem it just keeps on thinking that it has to use two jokes even though I have not said anything about that so we have to go to step four I guess nope keeps just tagging along on this side so it's not gonna be right this time let's just slap in the last prompt here and see what we get the conclusion here is that this prompt did not work for our Benchmark problem but of course this video was not about exactly how this result turned out it's more about how you can do some research with some different papers and try to come up with new prompting sequences skills techniques color whatever you want to try to solve a different problem I just found this very interesting something I'd just like to do and when I do this Benchmark I always like to do why didn't you just fill the six liter jug to get 6 liters I don't know what happened I just got a network error so I guess he didn't want to answer let's try to regenerate so it goes you're absolutely correct I appreciate your simplification of the problem my early responses were based on the premise of manipulating both jokes to reach the desired outcome it always says that thank you for your observation and I apologize for my confusion okay so that was better but anyway thank you for tuning in I hope maybe this is something you can be interested in I know it's a bit Niche but I had a lot of fun with testing out this and you can really learn something if you want to dive deeper into prompts different llm models this is not just applied to chat gbt you can use it for other things too but again thank you for tuning in check out some of my other videos on the screen here and I'll see you in the next one\n"