5 simple (but weird) ChatGPT-5 tricks to get a 10x better response

0:00 So, have you noticed that trying to get a really great response from chatbt5 just feels a little bit more difficult now? And that's even though this model is more powerful than all other previous models inside of chatbt, it's just become more tricky for a user to get more intelligence and unlock that intelligence out of it. And that's because GPT5 is fundamentally different when it comes to its architecture. So, today I want to share five simple but kind of weird tricks to get you a 10x better response from Chat GBT. And all these tricks have been shared directly from Open Eyes research team. So if you're interested in these small changes that get drastic improvements in how chatbt responds to you, let's just dive in. Okay, so first credit where credit is due. There's two blog posts here published by OpenAI where they talk about the details associated to prompting this tool and how to get the most out of the model. What I wanted to do here is I wanted to consolidate everything into five simple tricks where

0:45 you can get the most out of the tool based off of what they've shared. Before we jump into the tricks themselves, we need to first understand what's changed between what was chatpt in the past and what it is today. And first we want to look at the value gap. So what's happened is now users that don't necessarily know how to prompt effectively they they lose the value they can get from the tool. So this is about the amount of value they can get. But if you're an expert at prompting, you get a lot more from it. That will always probably remain the same. But the the gap between these two is drastically increased because of the way they consolidated all the models. And what do I mean by consolidated? Well, this is a simple visualization of what's happened here. We have on the lefth hand side all the legacy models that one can choose from the drop down. if you had access to plus or pro inside of chatbt. But what's happened now is we've

1:30 consolidated all eight of these models down into just three. And this is only three for those that have access to pro. If you have access to plus, you only have access to two of them. And you can see 40,4.15, 4.1 mini, 04 mini, 04 mini high have all been consolidated into the base model of GPT5. Then we have 03 that's been consolidated into GPT5 thinking. We have 03 Pro that's been consolidated into GPT5 Pro. Now, this consolidation means there needs to be some sort of routing that happens in the background. And that's the architecture we have set up here where what OpenAI has done is when you send in a prompt, they have a router in the middle that basically looks at your ask, looks at the context around the ask you've given it, and then routes it to a specific model. Once it's routed

2:15 it to a specific model, either the base model, the thinking one, or the pro one, it's also going to do two other things. It's going to set up a specific amount of reasoning for that model. So it could be minimal, low, medium, or high. And it's also going to set a feature of verbosity. So how much should it respond to you? How long should the response be? Low, medium, or high. So now the burden is on us to figure out different ways to prompt this model to ensure that we're getting routed to the best model for our use case. And we're hoping and ensuring that the reasoning level associated to that and the verbosity level are relevant and specific to our use case and our scenario. And that's where these tricks come into play. And that's where we're going to start with our first trick. So the first trick and the most obvious one is trigger words. So GPT5 and previous models as well have trigger words where if you mention a word, you're going to likely increase the

3:01 reasoning the model is going to use before it responds to you. And here are just four simple ones that I've seen OpenAI mention, but there are many others that you could use where if you tack this on at the back of your prompt, it's going to increase its reasoning. So thinking deeply, double check your work, be extremely thorough. This is this is critical uh to get to get right. These are all different things that we can use in our prompt to ensure that the model is going to be routed to a bigger model and likely is going to have a higher reasoning level to ensure that the response we get back is accurate to the task we've given it. So that's our first trick trigger words. Our next one is an optimizer. So this is a tool that OpenAI has provided to us. So we can use this tool and I think you may need API credits but it's maybe a dollar or two dollars. It's not that much. And what's going to happen is you're going to give them a base prompt and they're going to improve that prompt based off of best

3:48 practices for prompting GPT5. A few of the things they're going to do when they convert your prompt is they're going to focus on making sure that nothing is vague in the prompt. It's well structured. There's no contradictions in the prompt and a variety of other things. And on this slide, I wanted to give you an actual example and a demo of what this tool looks like. So here we have the original prompt. And the way you can get to this optimizer is through platform.ai.com. You can get access to the optimizer. So once you put in the original prompt, this the original prompt I put in. It's around basically rewriting drafts for me for a newsletter. Very basic. It's going to convert this once I select optimize into an improved prompt. It's not only going to improve it, but it's going to tell you exactly why it improved and what it changed. So here, if you collect the comment here, it's going to tell you exactly the reason as to why it changed and the improvement they made. A clear example here is that when I stated that

4:34 something was important associated to writing, I mentioned best, but I didn't specify what best meant. So what happened is their optimizer intuited what best meant based off of other things that I've talked about in that same sentence which was clarity, simplicity, and writing at a fifth grade reading level. It rearranged that initial prompt to state that that's what best correlates to. So it's being more specific. Another one here is it converted a minimal process that I provided into a detailed checklist that the AI will create for itself based off of what we care about when it comes to writing. And it'll then follow that checklist before it responds back to me. And there's a whole bunch of other things that it did. But that's the beautiful part about this optimizer is it's going to take the best practices, apply them to your prompt, and give you a huge boost in what you can get from the AI. So, if you take nothing else from this, this is a very easy and weird tool that you can use to put your base

5:19 prompt in, get an output, and it's going to have a much better response. Oh, hello there. Quick pause in your regular programming. This video is brought to you by MUA. So, below is a 30-day AI insight series that's completely free. So, if you click that link, you're going to get 30 days of 30 insights in your inbox of how you can apply AI to your business and your work. If that's at all interesting to you, check out the link. It's free. With that being said, let's get back into the video. So, that's our second one, prompt optimizer. Our third here is words matter. So, this one and the next one is associated to the way that this model follows instructions. So, GPT5 as well as previously GPT4.1, they're both very good at following instructions. So, whatever instructions you give them, they will follow it. Also, that means that if you put a contradiction into your prompt, it's going to overreason and overthink and try to and probably confuse itself

6:04 trying to figure out which direction to go based off the contradiction you provided in the prompt. The same thing applies to vague terms. And here's a simple example of what bad and good looks like when it comes to word usage. So, this is a bad prompt where we've stated, "Help me plan a nice party. Make it fun, but not too crazy." So, this lacks tons of specificity. And here's a slightly improved version of it where I say, "Okay, hey, I want you to help me plan a birthday party for my 8-year-old. There's going to be 10 kids there. The budget is $200. We have 2 hours, and it's going to be unicorn themed." By doing this, we're giving the AI a much better opportunity to solve our problem effectively without overreasoning or going in the wrong direction. So, when you're creating a prompt, either for a direct conversation with AI or as a system prompt in custom GPTs or GPT projects, make sure you're specific in the words that you use. So that's number

6:50 three, words matter. Number four is a structured prompt. So when it comes to structuring prompts, this is something that we've done for a while, but it's become more and more important as these models become very effective at following instructions. So I'd say this is most suitable for when you're creating custom GPTs or you're creating specific GPT projects. So we'll do GPTP and we'll do CGP for those two. And by doing this, XML has been by far the one that resonates most with GPT5 and that's what OpenAI recommends. And XML is basically a way of putting tags around your text. So here you have a bracket says context and this little slash is the end of the context. So this is the end. This is the beginning. This is the start of task. This is the end of task, start of format, end of format. And the reason this is important is by doing this the AI knows that this section is

7:36 dedicated to the background associated to the ask. It knows that this section is dedicated to the task it needs to achieve. And this section here, it knows that this is associated to the format it should provide you back when it responds. By doing this, we're going to improve the overall results that we get back from the AI because it comprehends the system instructions more effectively than if you didn't use this structure. And the beautiful part about all of this is you don't necessarily even know need to know how to do any of this. You can just ask AI. So once you've written your prompt, you've used the optimizer or something else. You can basically just send that off to AI and say, "Hey, convert this system prompt into XML." It'll do it for you. You can make minor tweaks if you want and then that can act as your system prompt for that custom GPT or GPT project that you create. So that's our fourth fourth trick. And then our fifth final trick is self-reflection. GPT5 is very good at critiquing itself

8:22 and we can use that against itself. So this is the way that I recommend doing and this is what OpenAI states is when you put a prompt together. So this is either a system prompt for a GPT project or a custom GPT or it could be a direct conversation. You state that the AI needs to create a rubric for itself and then judge itself on that rubric. And that's exactly the first step we're going to do. So we're going to say, "Hey AI, create a rubric based off of our intent." So in the example of writing, our intent was to ensure that we had a simple and clear piece that was written in a fifth grade reading level. That's our intent. So I want you to create a rubric aligned with that intent and then judge yourself against the rubric. That's exactly what this is going to happen. So it creates the rubric based off of that. Sets the standards, metrics, etc. And then when it writes its first draft based off the goal that I'm trying to achieve, which is writing newsletters in this use case, it's going

9:08 to write the first draft. After it's written the first draft, it's going to evaluate this first draft based off the rubric that was created. It's going to rate the response from 1 to 10. It's going to check against the rubric and also see if there are any gaps associated to what we're trying to achieve, what was written. What's going to likely happen is it'll iterate maybe two, three, four, five times. At the end of this, you're going to have your fifth response. And this fifth response is going to be probably the highest quality response you get back from the AI. And it's the only response you get back from the AI, which is also beneficial because everything else happens internally inside of the AI's head. and you only get the best piece. And with all these improved iterations, you're going to have a much higher quality output because of that self-reflection process and and internal critiquing. So that's our fifth and final trick, which is self-reflection in process. Key

9:53 takeaways here, right? The key takeaways are is that the foundational model has changed and the foundational model is focused on routing now. So we don't have access to the models we choose. That's happening in the background. So we need to improve our ability to prompt this model to ensure that in the back end it's directing it to the right model with the right reasoning level and the right verosity that we care about. And there's five simple ways we can do that. We can do that with trigger words. We can do that with a prompt optimizer that's very straightforward to use. We can do it with specific words and the right structure because remember they follow instructions effectively. And we also can use the self-reflection process to ensure that the model's critiquing itself to improve itself before we get the final output. So, those are just some of the tricks I wanted to share with you today. And with that being said, my friends, if you enjoyed this, please share with your friends. And also, as I mentioned previously, Below

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