How To Use GPT-o1 Preview (o1- Preview Tutorial) Complete Guide With Tips and Tricks

so in today's video we are diving into open ai's new 01 series models and these models are specifically trained to excel in deep reasoning and problem solve making them the perfect for tasks that require a lot of critical thinking like Advanced coding scientific reasoning and complex data analysis now the 01 series includes two models the 01 preview and the 01 mini the 01 preview is designed

to tackle difficult problems using a broad range of general knowledge meanwhile o1 Mini offers a faster more cost effective solution for tasks that are focused on coding Math and Science where broad knowledge isn't as crucial now these models are quite different to the traditional model you're used to with GPT 4 but while GPT 40 is still your go-to for fast responses image inputs and applications that require more versatility

the 01 modelss are designed to think deeply before responding they use a unique feature called reasoning tokens allowing them to break down a problem internally before providing an answer this makes them more ideal for complex tasks even if it takes a little bit longer to generate a response however keep in mind that the 01 models are currently in beta so some features are limited for example they only handle

text input and some Advanced functionalities like function calling aren't available yet today I'll guide you through how to use these 01 models effectively from setting them up into writing prompts that maximize their capabilities whether you're a developer or a beginner this video is going to help you and give you all the steps that

you need so let's dive into how you can Leverage The 01 models to tackle some of the most challenging problems out there so this is open ai's advice on prompting and I'm going to break down this entire thing step by step because although this information is really nice it doesn't give you all of the information you need to know so I'm going to go through in a quick presentation that's going to literally take 2 minutes each of these points and what they mean when you're prompting this new model so with this model what you want to do is you essentially want to ensure that you don't really follow all of your previous prompting guidelines because the games have changed and this is somewhat of a new era in prompt engineering so the first thing that opening ey says is that you must keep your prompts simple and direct what this basically means is that use short straight forward sentences or commands when asking the AI to do something so right here you can see I've got an example for this but basically what you want to do is you want to make sure that your prompts are really simple straightforward and not using prompt engineering techniques because this is already internally built into the model

so we can see a less effective prompt here which is can you please in a detailed and elaborate manner explain how photosynthesis Works considering all the biological and chemical processes involved this is a less effective prompt compared to simply stating explain how photosynthesis works now it might feel weird to do this because you might think that the model might not understand your query but the second prompt is shorter and gets directly to the point making it easier for the AI to understand what you're asking for these models already have these internal instructions to make them reason about every single query that they are given so when you add all of these unnecessary details that make the prompt less simple and Less Direct it often confuses the model resulting in a response that isn't as effective if you just asked it a simple question it does feel counterintuitive but this is from open eyes official documentation

now let's move on to point number two is that you want to avoid Chain of Thought prompting is where you'll tell an AI system or in this example GPT 40 or o1 think step by step and you don't want to ask the AI system to think step by step so for example you can see a less effective prompt is to think step by step and explain how you calculate the square OT of 16 this is a rather ineffective prompt for models like o1 the better prompt is what is the square root of 16 a simple question that doesn't ask the AI system to think step by step or to verify any other prompt engineering techniques that you may use it's better to Simply ask it the question and await your response the AI is already going to be doing all of the necessary reasoning steps that you might think to try not to over complicate it with Chain of Thought prompting additionally we need to use delimiters for clarity so open AI on their documentation they state that we need to use the limiters so what you need to do if you want a more effective prompt is to use special characters or formatting to separate different parts of your input to the AI so essentially

you can use XML tags and these tags are used in coding to structure data for example question marks at the start of a question section and question marks at the end so we can see a less effective prompt SL a less clear prompt is translate the text hello world and summarize this text but for a better prompt the we can see translate the text hello world we can see that this is in question marks then of course we've got another section summarize this text and we can see that this text is of course in quotation marks and we can see here that the quick brown fox jumps over the lazy dock so this is the kind of prompting that you want if you're trying to get the AI system to do many different things and by adding triple quotation marks you can clearly delimit the text to be translated and summarized so that the AI understands exactly what sections are related to which instruction if you don't do this the AI system might be a little bit confused on which bits it should do that to so please make sure that you do this for prompts that are a little bit more complex even with simple instructions you can see that the instructions here are quite simple but in order to make sure that the AI gets it right you're going to have to separate them and basically allow the AI to understand exactly what you're asking now we have

another one right here which is of course limit context from external sources so one thing that people might want to do is of course use external sources but when providing extra information for the AI to use keep it focused and only include what's most relevant now retrieval augmented generation involves the AI retrieving additional information from external documents or databases to answer a question more thoroughly and often times this does work with other models and other systems but providing too much context to IO is something that just isn't effective so we can see right here giving the model too much context and saying here is a 20page document on climate change and I want you to summarize the key points from the section on global warming but the rest is about unrelated topics like ocean and Forest management or if you have a long Corpus of text that you input and you ask it a question on a specific section that is just too much context for this model the better prompt to do which is right here is to basically just say summarize the key points about global warming from this excerpt and include that short paragraph or that short piece of text these models are already going to be using a lot of reasoning tokens so you want to make sure that you don't use too many tokens when you're using your prompts anyways until we get longer context windows in the future but the first example gives too much irrelevant information making it harder for the AI to focus and the second example is concise and only gives the necessary context improving the quality of the response so you could say reason on this

piece of text about yada yada or what do you think the implications for this are if you're currently confused between Which models you want to use because of course there is the 01 preview and mini the 01 preview is for deep reasoning and complex problem solving so this is tailored for situations where complex reasoning is essential and this is where the model excels in handling intricate multi-step problems that require deep thought and Brad general knowledge for example if you're working on tasks related to scientific research mathematical theorem proofs or Advanced Data analysis 01 preview can navigate these challenges by effectively considering multiple approaches before generating a response for broad general knowledge if your tasks involve leveraging a wide range of Knowledge from different fields the 01 preview is a better choice it's designed to use its internal reasoning tokens to evaluate various possibilities and provide wealth thought out answer this is particularly useful for applications like academic research legal analysis or complex decision-making systems where an expansive understanding is required now there are use cases requiring high accuracy where you want to be using A1 preview so when you need the model to deliver highly accurate outputs for critical applications the 01 preview is more suitable its ability to think thoroughly before answering means it can handle more nuanced queries making it IDE deal for generating precise and reliable results in fields like medicine engineering and Science and if your application can accommodate longer response times and you're looking to explore various Solutions or hypotheses 01 preview is the way to go the model is slower but it's more deliberate processing is perfect for environments where quality outweighs speed now let's talk about 01 mini this has faster processing for routine tasks 01 mini is a streamlined version of 01 preview that is optimized for Speed and cost it's

best used for coding math and science that doesn't require extensive background knowledge but can still benefit from the model's Advanced reasoning capability for example if you need to generate code Snippets form routine data validation or solve well-defined mathematical problems quickly 01 mini provides a more efficient solution it is also costeffective option for high volume applications if you have a high volume application that involves many any requests or require a cost effective solution 01 mini is the better choice its lower computational demands make it more economical to run especially for applications that don't require the depth of reasoning that 01 preview offer now when working on programming tasks debugging or implementing specific algorithms 01 mini excels it's designed to handle structured tasks quickly making it ideal for software development environments technical support systems or other applications with fast reliable responsib are needed so to conclude choose 01 preview when you need deep comprehensive reasoning a broad understanding of complex problems and high accuracy especially in fields like science Academia and advanced research or choose 01 mini when you require more cost-effective solutions for well- defined tasks in coding technical fields or routine problem solving scenarios so

let's actually take a look at some examples of where we can actually use these models and understand what they're doing effectively for example let's ask the model can I use Reddit to power my YouTube channel focused on AI so now that I've inputed this message right here you can see that the model has entered the phase where it is thinking so if we click this drop- down menu right here we can see that there are different stages to where this model is going to be thinking through different scenarios and of course thinking through different steps you can see right here that we won't always get the entire of the tokens open AI has said to us that we don't always get the internal workings of the model but we do see somewhat of an overview of how the model thinks about its strategy and you can see we then get a very comprehensive feedback that is long it's detailed and it gives us some nice responses on how we could improve our YouTube channel it talks about identifying relevant subreddits engaging authentic sharing our content thoughtfully be mindful of self-promotion rules hosting an AMA utilizing advertisements responding to feedback and there are just many different strategies that you can see under here such as following the 9:1 rule which I didn't even know about and of course many other sub areas that you might have not so these models the 01 series of models are remarkably effective when you ask them questions that do require lots of reasoning and can involve many different steps for difficult questions so if you want some prompt examples you can see right here that open AI has included these on their documentation tab so for example we've got code refractor and it says the open AI series of models are able to implement complex algorithms and produce code The Prompt asks 01 to refactor a react component based on some specific criteria you can see right here that this is the prompt where given the react component below change it so that the non-fiction books have red text return only the code in your reply do not include any additional formatting we can also see that there is code planning and you can see that I want to build a python app that takes user questions and looks them up yada yada we can also

see that for stem research there is a simple question and these are the kinds of prompts that you should be using if you're going to be looking at those fields you could always ask it to code different things this model does have really decent coding abilities so right here I'm asking it to code snake in Python that I can play you can see it's going to clarify the request and create the game and then crafting a snake game now a tidbit that I did want to add is that sometimes what the model might actually do unfortunately it might not actually think as hard as you want about certain question so you have to understand that certain questions are going to have certain tokens based on what the model thinks and if you do want the model to think harder about your problem you're going to have to include certain extra questions within those questions using delimiters like I've previously spoken about so you can see right here this one thought about it for 17 seconds and it managed to deliver the code which I could then use to play this game and it also gives me all of the exact steps needed to do slet to the final output something I should probably add is that you're not supposed to actually ask the model to explain its reasoning steps you can ask it to explain how it managed to get to the answer and explain why it thought that but you can't actually ask the model to explain all of the steps behind this because if you do you'll actually receive a warning in the email

from open AI they are trying to keep the model's reasoning token secret for whatever reason but it is something that I thought you guys should