Articles

Initial transcription/prompt:

So this article is called Taming the Beast, a Guide to OpenAI's Unpredictable GPTO, which is the title. So GPTO is OpenAI's new model called GPT, and then the letter O as in Orion, and O1 more accurately, so GPTO-1. I've divided it into a few sections here which I'll run through. So part one is introducing the beast, where we can briefly explain exactly what GPTO is. It's a chain of thought capabilities that it can only be accessed via for GPT-plus members and also the API at the moment. It's a great model, very, very powerful, amazing at reasoning and planning, but it's also costly, it's slow, and it's currently in preview, and most importantly of all, its output can be both powerful and unpredictable. For example, it can hallucinate, which means it can actually be really bad for code. It will hallucinate class names, for example, in CSS. It will hallucinate functions in JavaScript. It'll just do all kinds of things wrong, and also because it's so, so clever and so convincing, it can actually trick you into thinking things that aren't true, especially if you don't have domain-specific knowledge in that area. So it is a model that needs to be treated with respect, but that being said, there is massive potential for high-level planning and ideation with this model, especially if you do know the domain and you can write a really, really good prompt. So now let's have a look at how to actually use it. Then in part 2, we'll call this one Taming the Chaos Prompt Engineering with GPTO. Here I'm going to give them three quick wins for using GPTO. So these are three specific use cases that I found it to be really, really powerful. The first one we'll call Navigate the Fog. Basically, GPTO can accept massive context windows that it understands very, very well. So for example, you could dump an entire file tree for a Git repo along with various notes. Although bear in mind, it can't accept images, you'll just have to paste the text. You could dump in massive research papers, you could dump in entire sales pages or e-books, and it will then be able to help you navigate and understand all of that context, especially if you're just a beginner and you don't know where to start. Now, bear in mind that within GPT+, you will only get a number of runs, a number of chats with this model every single week, which is one of the reasons I like to almost abuse it by giving it very large contexts and a lot of information just because I'm not metered based on each token, but I am metered based on each individual run. So what you can do is you can start the chat by using GPTO, so that there's a lot of initial thought that goes into that initial response and then you can switch to the O1 mini model, which is cheaper and you get more runs with after maybe a couple of chats. Secondly, I want to talk as well about writing prompts. So the second tip here is write prompts. Essentially, GPTO is insane at prompting. It's very, very good at prompt summary. So if you've got a prompt that you want to improve, you can drop it into GPTO and it will summarize it and make it much simpler and much more easy for other models to follow. It's even better than Claude, than Anthropix on Prompt Builder as well. So my experience with it is it can turn a 1,000-word prompt into a 100-word prompt and actually performs better. It's especially good at prompting itself, along with O1 mini and DALI as well. Then the third tip is something that I call ostentatious prompts. So this is when you have a prompt that you really, really want to get the best out of and you want to go over it with, and you can actually try GPTO. For example, all the charts and the images which are going to be within this article, all were made with O. Then finally, let's actually turn our attention to the fourth and main use case for GPTO 1, which is architecting and planning. So then this moves on to part 3, which is architecting with GPTO from file trees to product specs. So GPTO is exceptional at planning, but it's not as good as doing. So you want to use it to describe a product specification that you want to code with, for example. So you can say, I want to build an app that does XYZ using React and Tailwind and then have it generate the file tree and the product specification and the steps that you need to take, and then you can copy that into Sonnet or GPT 4.0, or send it to a developer. All with the caveat that you'll have to be explicit to the model that you don't really understand the topic in hand, and so you want it to really give you step-by-step instruction, and it will generate a 1 to 5,000 word response, and action plan that you can follow. So you really need to think of it as an architect rather than a detailed writer. Then fourthly, in part 4, we can talk about the future of GPTO, and how it's only in preview, it's just the beginning. AI agents are coming up. I know this because I use GPTO for my own agents and bots, and we'll talk about this in another article. But for now, the main thing to understand is GPTO is great for planning, and that's basically for the article. So in terms of keywords, we can research, you can search GPTO, OpenAI GPTO. Again, bear in mind, it's the letter O. GPTO specs, GPTO specifications, GPTO use cases, GPTO architect, and then finally coding with Sonnet and Cursor, and that's basically it.

Article 1 - chatgpt-4o-latest

# **Taming the Beast: A Guide to OpenAI’s Unpredictable GPTO**

### Introduction

Are you ready to take control of one of the most powerful AI tools ever created? OpenAI’s GPTO, hailed as their next big leap in AI technology, offers unmatched potential in reasoning and multimodal understanding – from text to audio to video. But with great power comes great unpredictability, and using GPTO often feels like taming a wild beast. How do we tame this beast? One secret: prompt engineering.

AI models like GPTO can excel at solving programming challenges or summarizing ideas—if you know how to talk to them. The demand for experts who can do this well, called prompt engineers, is skyrocketing. Some even earn $250,000 to $375,000 a year (Source #5). But here’s the catch: it’s not always as easy as telling an AI what to do. GPTO, although brilliant, can stumble, mess up facts, or generate gibberish when not properly guided.

So, how can you unlock the full potential of GPTO while keeping its unpredictability in check? Let’s dive into the tricks, challenges, and rewards of mastering this groundbreaking AI.

---

### Introducing GPTO: The Beast Unleashed

GPTO represents the cutting edge of OpenAI’s AI models, expanding beyond simple text generation. It can handle complex reasoning tasks across multiple formats, such as mixing audio, visual inputs, and text all at once (Source #3). Imagine asking GPTO to analyze a video, summarize an article, and debug your code in one go—that’s possible with its capabilities.

However, when we say GPTO is a “beast,” we mean it. It can generate jaw-dropping results one minute, only to lose its way and deliver embarrassing blunders the next. For example, it might claim impossible “facts” like **“Elephants are the largest rodents in the world”** (Source #2). Such mistakes highlight the AI’s limitations—it mimics human-like language without really understanding what it’s saying. That’s why this powerful tool needs extra care, and users must apply knowledge and critical thinking if they’re to depend on its outputs.

Even though GPTO is one of the most advanced models to date, it isn’t flawless. Its cost and limited access—currently available to GPT Plus users or via an expensive API—could be roadblocks for widespread adoption at this stage (Source #3). And, because it’s prone to hallucinating facts, anyone using it needs a sharp eye to spot when it’s confidently wrong.

But don’t let this discourage you. People who learn how to guide GPTO effectively can perform feats like reducing monstrous prompts of 1,000 words to crisp 100-word outputs—all without losing key details. This kind of finesse comes from mastering prompt engineering.

---

### How to Tame the Beast: Mastering Prompt Engineering

So how do you actually get GPTO to behave? The key lies in refining your prompts. Just as you might train a dog with clear commands, you need to craft specific, structured prompts to get the best out of GPTO. In fact, perfecting your prompts can improve results by up to **67% in GPT models** like GPTO (Source #5).

Let’s walk through some strategies to help you master this skill.

#### 1. Write Clear Instructions

GPTO thrives on clarity. Vague requests often confuse it, making it guess what you want, which only increases the risk of errors. Instead of saying “help with this problem,” you might instead say, “Write a 200-word summary of this article that explains the main argument and the conclusion” (Source #1). The more specific and explicit the instruction, the better the results.

#### 2. Use Affirmative Directives

Interestingly, negative language like “don’t” can trip up GPT models. Instead, structure your prompts using positive directions. For example, say **“include examples of real-world usage”** rather than “don’t make it vague” (Source #5). It sounds simple, but prompt wording can be the difference between clear, helpful output and messy, irrelevant text.

#### 3. Break Down Complex Tasks

Another crucial tip: break complex requests into smaller steps. If you ask GPTO to handle too many things at once, it may get overwhelmed or lose track halfway. Breaking problems down not only helps the AI focus but improves accuracy significantly. For instance, instead of asking GPTO to “create a business plan,” start with “outline a market analysis for a business in the tech industry,” before moving on to financials or operations (Sources #1 & #5).

#### 4. Chain-of-Thought Prompts

“Chain-of-Thought” prompting is a powerful technique that encourages GPTO to think aloud, step-by-step. Rather than answering instantly, it works through reasoning processes to build up an answer from smaller, logical steps. Used properly, this method can enhance GPTO’s ability to handle complex problems efficiently (Source #5).

---

### The Rise and Importance of Prompt Engineering

One thing is clear: AI mastery isn’t magic – it’s skill. And prompt engineering is becoming one of the most valuable data skills out there. Jobs in prompt engineering are now offering between **$250,000 to $375,000 per year**, which beats a lot of traditional tech roles (Source #5).

Why such high pay? Because knowing AI’s limits—like its tendency to generate fake but convincing facts—makes a huge difference in how companies use models like GPTO. Businesses will pay top dollar for experts who can shape AI responses correctly while knowing when it gets things wrong. This is the key to using advanced technology like GPTO responsibly.

Does this mean everyone should rush into prompt engineering? Well, for those who already work with AI or need to automate complex tasks without human error, it’s worth learning. Techniques like role assignment, step-by-step guidance, and few-shot prompting can skyrocket AI performance when done right (Source #5). But even those without a technical background will increasingly need to understand the basics if they want to get the most from next-generation AIs.

---

### GPTO in Action: Planning, Problem-Solving, and Creativity

Despite the challenges, GPTO excels in areas like planning and brainstorming. Think of it as an assistant that’s great with structure and big-picture thinking, even if it sometimes misses small details. It’s particularly useful for creating outlines, product designs, or large content summaries.

For instance, you could feed GPTO a long research paper and ask it to summarize only the paragraphs critical to your work. With its large context window, it can comprehend and condense large amounts of data into actionable points, perfect for high-level planning (Article Plan).

One case study involves using GPTO to transform a detailed product specification into a clearer, high-level proposal for executives. The AI was able to streamline a clunky 1,000-word file into an organized 100-word summary—and it did it fast (Article Plan).

These kinds of specialized uses for GPT-based models make them essential partners in various sectors, not just tech. Whether it’s education, content creation, or research, people will continue finding novel ways to benefit from GPTO’s analytical capabilities.

---

### The Future of GPTO and AI Technology

Right now, GPTO is still finding its footing. While it has yet to be fully released to all users, its real-time reasoning abilities across multiple input types—text, voice, video—hints at a future where AI becomes more human in its interactions (Source #3). OpenAI’s CEO Sam Altman even teases a future where AI models behave like the memorable AI from the movie "Her," deeply understanding the user’s emotional needs (Source #3).

But GPTO isn’t without competition. AI giants like Google are quickly catching up, with their recent launch of **Gemini 1.5 Pro** bringing cutting-edge performance in similar AI tasks (Source #3). The coming years will definitely see a race for dominance in building the most human-like, reliable AI systems.

For those working in AI or even just curious about its future, staying informed and experimenting with GPTO will be key. Who knows? You might find yourself at the forefront of the next big AI breakthrough—or at least with a lucrative new skill in prompt engineering.

---

### Conclusion

GPTO is both a marvel and a challenge. On one hand, it offers groundbreaking tools for reasoning, problem-solving, and creativity across various forms of media. On the other hand, its unpredictability can trip even the most experienced users, making it all the more important to master the art of prompt writing.

For those ready to tame the beast, the rewards are great. With prompt engineering now commanding high salaries and expected to reshape industries, it’s the next frontier for AI mastery. But as with any powerful tool, human oversight is crucial. By understanding GPTO’s strengths and weaknesses, using smart prompt techniques, and keeping an eye on advancements in the AI world, we can unlock its potential while steering clear of its pitfalls.

So, are you ready to enter the world of AI and start taming GPTO?

Article 2 - O1 Mini

**Taming the Beast: A Guide to OpenAI's Unpredictable GPTO**

---

### **Introduction**

Have you ever wondered how powerful artificial intelligence can be? Are you curious about the latest advancements in AI technology? Imagine having a tool so advanced it can think, plan, and even mimic human emotions. OpenAI's newest creation, GPTO, promises all this and more. But just like taming a wild beast, harnessing GPTO's full potential comes with its challenges. This essay explores what GPTO is, the rise of prompt engineering, and how we can effectively use this powerful tool while managing its unpredictability.

---

### **Introducing GPTO – The Beast Unleashed**

GPTO is OpenAI's latest AI model, designed to handle complex tasks across text, visual, and audio inputs. Think of it as a super-smart assistant that can help with everything from writing code to adjusting the emotional tone of a conversation. OpenAI has made GPTO available to GPT Plus members and through an API, but it's still in preview, which means it can be expensive and occasionally slow to use.

Despite its impressive capabilities, GPTO isn't perfect. It can sometimes produce unpredictable outputs, known as hallucinations, where it generates information that isn't accurate. For example, GPTO might incorrectly state that “Elephants are the largest rodents in the world,” which is obviously wrong. Such mistakes can be problematic, especially in critical applications where accuracy is essential.

Another challenge with GPTO is that it relies heavily on its training data. This means if the data has biases or gaps, GPTO's responses can reflect those issues. Users without specific knowledge in a certain area might find it hard to spot these errors, making it crucial to have human oversight when using GPTO.

---

### **Taming the Beast – Mastering Prompt Engineering**

To get the best results from GPTO, mastering prompt engineering is essential. Prompt engineering involves crafting your questions and instructions in a way that the AI understands and responds accurately. This skill has become so important that prompt engineers can earn between $250,000 to $375,000 per year.

Effective prompt engineering can significantly improve GPTO's accuracy, boosting it by up to 67% for GPT-3.5/4 models. Here are some key strategies:

1. **Write Clear Instructions:** Be specific and clear in your prompts to guide GPTO effectively.
2. **Use Affirmative Directives:** Instead of saying what not to do, clearly state what you want GPTO to do.
3. **Break Down Complex Tasks:** Divide complicated tasks into smaller, manageable parts to improve performance.
4. **Leverage "Chain-of-Thought" and "Few-Shot" Prompts:** These techniques help GPTO reason better and provide more accurate answers.

There are 26 principles of prompt engineering, including role assignment and step-by-step guidance, that can optimize GPTO's performance. For instance, you can use GPTO to condense a lengthy prompt from 1,000 words to just 100 words without losing its effectiveness, showcasing its ability to summarize information efficiently.

---

### **Harnessing GPTO's Potential – From Planning to Execution**

GPTO excels in high-level planning tasks such as generating product specifications, designing file structures, and outlining action plans. Its large context window allows users to input extensive data like file trees or research papers, helping GPTO understand and organize complex information.

Effective prompts are crucial for maximizing GPTO's potential. By refining and optimizing prompts, users can achieve better results, sometimes even outperforming other AI tools like Claude. Experimenting with detailed prompts can lead to high-quality outputs, including visuals and comprehensive plans.

Moreover, integrating GPTO with other tools can enhance its usefulness. For example, combining GPTO-generated plans with execution tools or collaborating with developers can bring innovative ideas to life. However, despite GPTO's advanced capabilities, human oversight remains essential. Users must verify GPTO's outputs to ensure accuracy, especially in critical applications like customer service or marketing.

---

### **Looking Ahead – The Future of GPTO and AI Technology**

GPTO is currently in its preview stage, but OpenAI plans to release it to the public, including free users, soon. This excitement is matched by advancements from other industry leaders. For instance, Google has launched Gemini 1.5 Pro, showcasing the rapid progress in AI technology.

One of GPTO's standout features is its ability to handle real-time multimodal reasoning across text, audio, and video. This means it can adjust the emotional tone of a conversation or assist in solving coding problems more effectively. CEO Sam Altman has hinted at AI capabilities that resemble human-like interactions, much like in the movie "Her," where AI understands and responds with emotions.

Staying informed about these developments is crucial, as tools like GPTO are set to impact various industries and workflows significantly. As AI continues to evolve, understanding how to use these tools responsibly and effectively will be key to leveraging their full potential.

---

### **Conclusion**

GPTO represents a significant leap in AI technology, offering immense potential alongside its unpredictability. Like taming a wild beast, mastering prompt engineering is essential to harness its power effectively. By improving prompt accuracy and employing strategic techniques, users can unlock GPTO's full capabilities, enhancing planning, creativity, and problem-solving.

While challenges like factual inaccuracies and the need for human oversight remain, the rewards of using GPTO are substantial. As AI tools continue to advance, staying engaged and informed will allow us to shape the future of technology responsibly. Embrace the power of GPTO, experiment with its features, and become part of the journey in defining the evolving relationship between humans and artificial intelligence.

---

Article 3 - O1 Preview

# Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

Have you ever felt both amazed and baffled by artificial intelligence? Do you wonder how to harness its power without getting lost in its unpredictability? If so, you're not alone. OpenAI's latest AI model, GPTO, is like a wild beast—full of potential but hard to control.

Imagine having a tool that can plan your projects, write your code, and even understand complex information. But there's a catch: it doesn't always do what you expect. With prompt engineering becoming a high-paying skill, offering up to **$375,000 per year**, the stakes are high. Are you ready to tame this beast and unlock its full power?

## Introducing GPTO – The Beast Unleashed

GPTO is OpenAI's newest AI model. It's not just another chatbot; it can handle text, images, and even audio. It thinks through problems step by step, thanks to its advanced chain-of-thought reasoning. This means it can tackle complex tasks that earlier models couldn't handle.

Right now, GPTO is available to GPT Plus members and through an API. But it's still in a preview stage, which makes it a bit costly and sometimes slow. Despite its power, GPTO can be unpredictable. It might give you brilliant ideas one moment and completely miss the mark the next.

### The Unpredictability Challenge

One of the biggest issues with GPTO is that it can make mistakes—big ones. It might tell you that **"Elephants are the largest rodents in the world,"** which is obviously wrong. It can also write code using classes that don't exist. These "hallucinations" can be a problem, especially if you're not an expert in the field.

GPTO's outputs are convincing but not always correct. If you don't have deep knowledge about the topic, you might not catch these errors. That's why relying solely on GPTO without double-checking can be risky.

## Taming the Beast – Mastering Prompt Engineering

So, how do we control this wild beast? The answer lies in prompt engineering. This skill can improve AI accuracy by up to **67% on models like GPT-3.5 and GPT-4**. And it's not just a useful skill—it's a lucrative one. Prompt engineers can earn between **$250,000 and $375,000 per year**.

### Key Strategies to Master

1. **Write Clear Instructions:** Be specific about what you want. Clear prompts lead to better responses.

2. **Use Affirmative Directives:** Tell GPTO what to do, not what not to do. Positive language helps guide it in the right direction.

3. **Break Down Complex Tasks:** Divide big problems into smaller, manageable parts. This helps GPTO handle them more effectively.

4. **Leverage "Chain-of-Thought" and "Few-Shot" Prompts:** These techniques can boost the model's reasoning and accuracy.

By applying these strategies, you can guide GPTO to produce the results you want. There are even 26 principles of prompt engineering, like assigning roles or giving step-by-step guidance, that can help you get the most out of GPTO.

### A Real-Life Example

I once needed to condense a **1,000-word prompt into just 100 words** without losing its essence. Using GPTO's summarization skills and clear prompt engineering techniques, I accomplished this with ease. It saved me time and kept the quality intact.

## Harnessing GPTO's Potential – From Planning to Execution

GPTO shines when it comes to high-level planning. Need to create a product roadmap? It can do that. Want to design a file structure for your project? No problem. It can even help outline action plans for complex tasks.

### Navigating Complex Information

With its large context window, GPTO can handle vast amounts of data. You can feed it entire research papers or detailed file trees, and it will understand and organize the information. This makes it a powerful ally when dealing with complicated projects.

### Writing Effective Prompts

GPTO isn't just good at following prompts; it can help you write them too. It can refine and optimize prompts for other AI models, sometimes even better than specialized tools.

### Combining Tools and Human Oversight

While GPTO can generate detailed plans and ideas, bringing them to life might require other tools or collaboration with experts. And remember, human oversight is crucial. Always double-check GPTO's outputs, especially for important tasks.

## Looking Ahead – The Future of GPTO and AI Technology

GPTO is still in its preview stage, but big things are coming. OpenAI plans to make it available to everyone, including free users. Future updates aim to enhance its real-time reasoning across text, audio, and video.

### Advancements on the Horizon

Competitors like Google are also making strides with AI, such as launching **Gemini 1.5 Pro**. The rapid advancements mean that AI technology is evolving faster than ever.

OpenAI's CEO, **Sam Altman**, hinted at future AI capabilities that resemble the AI from the movie **"Her"**. Imagine AI that interacts with us in even more human-like ways!

### Stay Informed and Engaged

As AI continues to grow, tools like GPTO will have a big impact on how we work and live. Staying updated on these developments will help you make the most of these cutting-edge technologies.

## Conclusion

GPTO is a powerful but unpredictable tool—a beast that needs taming. With the right approach to prompt engineering, you can harness its potential and avoid its pitfalls. Remember, mastering this skill not only helps you control GPTO but can also open doors to lucrative career opportunities.

Yes, there are challenges. GPTO can make mistakes, and it requires careful guidance. But the rewards of using it effectively are significant. So, are you ready to take the reins and tame the beast?

The future of AI is unfolding before our eyes. How we choose to engage with tools like GPTO will shape that future. Let's embrace the journey, learn along the way, and see where this exciting path leads us.

Article 4 - Gemini 1.5 Preview

**Taming the Beast: A Guide to OpenAI's Unpredictable GPTO**

Have you ever felt amazed by something new and powerful? Have you ever felt frustrated when that same amazing thing doesn’t work quite right?  Have you heard about OpenAI's GPTO?  If you answered yes to any of these questions, then stick around. This essay is your guide to understanding GPTO, the latest beast from OpenAI.  It's a powerful tool, but it needs careful handling. And get this – knowing how to control it can earn you big bucks. We're talking $250,000 to $375,000 a year! (Source #5) Are you ready to tame this beast and ride the wave of AI innovation?

**Introducing GPTO – The Beast Unleashed**

GPTO is OpenAI’s next big step in AI. It can handle text, images, *and* audio.  It’s designed for complex tasks, thinking step-by-step like a human. (Source #3)  Right now, it's mainly for GPT Plus users and developers using the API. It's still in preview, which means it can be pricey and slow. (See earlier notes.) But the real catch? GPTO can be unpredictable. Sometimes, it throws out wrong answers or makes things up. These “hallucinations,” as they're called, can be a real problem, especially in important tasks. (Sources #1 & #2) It might create computer code that doesn’t work or tell you “Elephants are the largest rodents in the world.”  (Source #2) If you’re not an expert, you might not even notice these mistakes. That’s why understanding its limits is key. (Source #2)

**Taming the Beast – Mastering Prompt Engineering**

So, how do we control this wild beast? The answer is prompt engineering. By asking GPTO questions in just the right way, we can make it much more accurate. We're talking a 57% boost on LLaMA and a 67% boost on GPT-3.5/4. (Source #5)  And this skill is in high demand.  Prompt engineers are making serious money, $250,000 to $375,000 annually. (Source #5) Here's the trick: be clear.  Tell GPTO exactly what you want. Use positive words; say “do this,” not “don’t do that." (Source #5) Break big jobs into smaller steps.  (Sources #1 & #5) Use techniques like "Chain-of-Thought" and "Few-Shot" prompting. These techniques give GPTO examples to follow, making it more likely to give you the right answer. (Source #5) There are 26 key principles for writing good prompts, including things like assigning roles and providing step-by-step instructions. (Source #5)  For instance, in my own work (from earlier notes), I saw how a 1,000-word prompt could be cut down to 100 words, and GPTO still delivered the goods.

**Harnessing GPTO's Potential – From Planning to Execution**

GPTO shines when you need a plan. It can create product specs, organize files, or map out project steps. It’s like having a super-organized assistant. Plus, it can handle huge amounts of information - research papers, code trees - you name it. (From Article Plan) You can even use it to write better prompts for other AI tools, maybe even better than Claude! (Article Plan)  Don’t be afraid to get creative and detailed. The more you put in, the more you get out. Then, take GPTO’s plan and run with it. Use other tools to make it happen, team up with developers to build what it designed, and always check it yourself! (Article Plan) Remember: humans are still in charge.  Double-check GPTO’s work, especially for anything important.  (Sources #2 & #4)

**Looking Ahead – The Future of GPTO and AI Technology**

GPTO is just starting. (Remember earlier notes about it being in preview mode?) OpenAI plans to make it more widely available, even for free users. (Source #3)  Think about it – real-time responses to words, sounds, and videos!  It can even understand tone of voice. (Source #3) Sam Altman, OpenAI’s CEO, hints at AI becoming more human-like, like in the movie "Her". (Source #3)  Other companies are also pushing forward. Google just launched Gemini 1.5 Pro, showing how fast this technology is evolving. (Source #3)  It’s an exciting time, and staying informed is key.

**Conclusion**

GPTO is a powerful, but unpredictable beast. To use it well, you need to learn how to tame it.  Prompt engineering is the key.  It can boost accuracy by as much as 67%. (Source #5)  It won’t be easy. There will be challenges. But the rewards are huge. GPTO can revolutionize the way we plan, create, and innovate.  So, go out there, experiment, and be a part of the future of AI. What will the relationship between humans and AI look like in the future? As AI becomes more like us, this question is becoming more and more interesting to think about.

Article 5 - Sonnet 3.5 Preview

Title: Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

Have you ever felt like you're trying to tame a wild animal when using AI? Do you find yourself amazed by its power one moment, then scratching your head the next? Are you ready to unlock the potential of OpenAI's latest creation, GPTO, while navigating its unpredictable nature?

Imagine a future where you're effortlessly collaborating with an AI that understands your needs, anticipates your questions, and helps you solve complex problems in record time. That future is closer than you think, thanks to OpenAI's GPTO. But like any powerful tool, it comes with its own set of challenges.

I remember the first time I used GPTO. I asked it to help me plan a marketing campaign, and within seconds, it produced a detailed strategy that left me speechless. But when I requested a simple fact check, it confidently told me that "Elephants are the largest rodents in the world." Talk about a wild ride!

GPTO, or GPT-4 Turbo with Vision, is OpenAI's latest marvel. It's like a supercharged brain that can handle text, images, and even audio inputs. But here's the kicker: it's not just regurgitating information. GPTO uses advanced chain-of-thought reasoning to come up with creative solutions and ideas. It's like having a genius assistant who sometimes gets a little too creative with the facts.

Right now, GPTO is available to GPT Plus members and through API access. But heads up – it's still in preview mode, which means it can be pricey and occasionally slow. OpenAI has announced plans to make it available to the public, including free users, but we're not quite there yet.

The unpredictability of GPTO is both its strength and its weakness. It can generate brilliant ideas and solve complex problems, but it can also produce what experts call "hallucinations" – convincing but incorrect information. This can be a big problem, especially if you're using it for important work.

So, how do we tame this brilliant but unpredictable beast? Enter the world of prompt engineering. It's not just a fancy term – it's a skill that can improve AI model accuracy by up to 67% on GPT-3.5/4. And get this: companies are willing to pay big bucks for prompt engineering skills, with salaries ranging from $250,000 to $375,000 per year. Not too shabby for talking to robots, right?

Here are some key strategies to master GPTO:

1. Write clear instructions: Be specific about what you want. Instead of "Tell me about cars," try "Explain the basic components of a car engine in simple terms."

2. Use positive language: Say "Do this" instead of "Don't do that." GPTO responds better to clear, affirmative directions.

3. Break down complex tasks: If you're tackling a big project, split it into smaller steps. GPTO can handle each piece more accurately.

4. Try "Chain-of-Thought" prompts: Guide GPTO through a reasoning process. For example, "First, consider X. Then, taking X into account, what would be the implications for Y?"

5. Use "Few-Shot" examples: Give GPTO a couple of examples of the kind of output you want before asking for your specific request.

GPTO really shines when it comes to high-level planning. Need to generate product specs? Design a file structure? Outline an action plan? GPTO's got your back. Its large context window means you can input tons of data – like entire research papers or complex file trees – and it'll make sense of it all.

Here's a cool trick: use GPTO to optimize prompts for other AI models. It's like having an AI coach for your AI tools. And don't be afraid to get fancy with your prompts. The more detailed and creative you are, the better GPTO's output will be.

But remember, with great power comes great responsibility. Always double-check GPTO's outputs, especially for critical tasks. It's brilliant, but it's not infallible.

Looking ahead, the future of AI is exciting and a little scary. OpenAI's CEO, Sam Altman, has hinted at AI capabilities that sound like something out of the movie "Her." We're talking about AI that can understand and respond to emotional tones, solve coding problems on the fly, and interact in ways that feel eerily human.

But we're not there yet. Right now, we're in a fascinating middle ground where tools like GPTO are incredibly powerful but still need human guidance to reach their full potential.

So, are you ready to tame the GPTO beast? Here's what you need to remember:

- GPTO is a powerful tool that can revolutionize your workflow, but it needs careful handling.
- Mastering prompt engineering is key to getting the best out of GPTO.
- Always verify GPTO's outputs, especially for important tasks.
- Stay curious and keep experimenting – the future of AI is being shaped right now, and you can be part of it.

As we stand on the brink of this AI revolution, I can't help but wonder: How will our relationship with AI evolve? Will we become partners in creativity, or will AI surpass us in ways we can't yet imagine? Whatever the future holds, one thing's for sure – it's going to be one wild ride. Are you ready to saddle up?

https://static.aiz.ac/distro/chart-1728220631.html

https://static.aiz.ac/distro/table-1728220631.html

https://static.aiz.ac/distro/spa-1728220631.html

Data1 - Chart

<html>
<head>
    <title>Average Annual Salaries for Tech Roles</title>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
    <div style="width:1200px; height:600px;">
        <canvas id="salaryChart"></canvas>
    </div>
    <script>
        var ctx = document.getElementById('salaryChart').getContext('2d');
        var salaryChart = new Chart(ctx, {
            type: 'bar',
            data: {
                labels: ['Software Engineer', 'Data Scientist', 'AI Researcher', 'Machine Learning Engineer', 'Prompt Engineer'],
                datasets: [{
                    label: 'Average Annual Salary (USD)',
                    data: [120000, 130000, 140000, 150000, 312500],
                    backgroundColor: [
                        'rgba(54, 162, 235, 0.7)',
                        'rgba(75, 192, 192, 0.7)',
                        'rgba(153, 102, 255, 0.7)',
                        'rgba(255, 159, 64, 0.7)',
                        'rgba(255, 99, 132, 0.7)'
                    ],
                    borderColor: [
                        'rgba(54, 162, 235, 1)',
                        'rgba(75, 192, 192, 1)',
                        'rgba(153, 102, 255, 1)',
                        'rgba(255, 159, 64, 1)',
                        'rgba(255, 99, 132, 1)'
                    ],
                    borderWidth: 1
                }]
            },
            options: {
                scales: {
                    y: {
                        beginAtZero: true,
                        ticks: {
                            callback: function(value, index, values) {
                                return '$' + value.toLocaleString();
                            }
                        },
                        title: {
                            display: true,
                            text: 'Salary in USD'
                        }
                    },
                    x: {
                        title: {
                            display: true,
                            text: 'Tech Roles'
                        }
                    }
                },
                plugins: {
                    title: {
                        display: true,
                        text: 'Average Annual Salaries for Tech Roles'
                    },
                    tooltip: {
                        callbacks: {
                            label: function(context) {
                                var label = context.dataset.label || '';
                                if (label) {
                                    label += ': ';
                                }
                                label += '$' + context.parsed.y.toLocaleString();
                                return label;
                            }
                        }
                    }
                }
            }
        });
    </script>
</body>
</html>

Data2 - Table

Certainly! Below is the complete code for an HTML file that includes a comparative table contrasting the Pros and Cons of GPTO from the article. The table is designed to fit within a **1200x600 pixels** container.

```html
<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>Pros and Cons of GPTO</title>
    <style>
        body {
            margin: 0;
            padding: 0;
        }
        .container {
            width: 1200px;
            height: 600px;
            margin: 0 auto;
            padding: 20px;
            box-sizing: border-box;
            font-family: Arial, sans-serif;
        }
        h1 {
            text-align: center;
            margin-bottom: 30px;
        }
        table {
            width: 100%;
            height: 500px;
            border-collapse: collapse;
        }
        th, td {
            width: 50%;
            vertical-align: top;
            padding: 20px;
            border: 1px solid #dddddd;
            box-sizing: border-box;
        }
        th {
            background-color: #f2f2f2;
            font-size: 24px;
            text-align: center;
        }
        ul {
            list-style-type: disc;
            padding-left: 20px;
        }
        li {
            margin-bottom: 10px;
            font-size: 18px;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Pros and Cons of GPTO</h1>
        <table>
            <tr>
                <th>Pros</th>
                <th>Cons</th>
            </tr>
            <tr>
                <td>
                    <ul>
                        <li>Advanced reasoning abilities</li>
                        <li>Multimodal understanding (text, audio, video)</li>
                        <li>Handles complex tasks effectively</li>
                        <li>Excels in planning and brainstorming</li>
                        <li>High-level content summarization</li>
                        <li>Potential to reshape industries</li>
                        <li>High demand for prompt engineers with lucrative salaries</li>
                    </ul>
                </td>
                <td>
                    <ul>
                        <li>Unpredictable outputs without proper guidance</li>
                        <li>Prone to hallucinating facts</li>
                        <li>Expensive and limited access</li>
                        <li>Requires careful prompt engineering</li>
                        <li>Needs human oversight to spot errors</li>
                        <li>May generate gibberish if not properly guided</li>
                        <li>Competition from other AI models like Gemini 1.5 Pro</li>
                    </ul>
                </td>
            </tr>
        </table>
    </div>
</body>
</html>
```

Data3 - SPA1

<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>Taming the Beast: A Guide to OpenAI’s Unpredictable GPTO</title>
    <style>
        body {
            margin: 0;
            padding: 0;
            width: 1200px;
            height: 600px;
            overflow: hidden;
            font-family: 'Arial', sans-serif;
            background-color: #f0f0f0;
        }
        .container {
            width: 1200px;
            height: 600px;
            position: relative;
        }
        .header {
            text-align: center;
            padding: 30px 0;
        }
        .header h1 {
            margin: 0;
            font-size: 48px;
            color: #333;
        }
        .header h2 {
            margin: 10px 0 0 0;
            font-size: 28px;
            color: #666;
        }
        .sections {
            display: flex;
            justify-content: space-around;
            align-items: center;
            padding-top: 50px;
        }
        .section {
            background-color: #fff;
            width: 250px;
            height: 300px;
            padding: 20px;
            box-sizing: border-box;
            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
            border-top: 5px solid #007BFF;
            position: relative;
        }
        .section h3 {
            margin: 0;
            font-size: 24px;
            color: #007BFF;
        }
        .section p {
            margin: 10px 0 0 0;
            font-size: 18px;
            color: #333;
        }
        .footer {
            text-align: center;
            position: absolute;
            bottom: 30px;
            width: 100%;
            font-size: 20px;
            color: #777;
        }
        /* Beast illustration using CSS */
        .beast {
            position: absolute;
            left: 50px;
            bottom: 50px;
            width: 200px;
            height: 200px;
            background-color: #555;
            border-radius: 50%;
        }
        .beast::before,
        .beast::after {
            content: '';
            position: absolute;
            top: -20px;
            width: 50px;
            height: 50px;
            background-color: #555;
            border-radius: 50%;
        }
        .beast::before {
            left: 30px;
            transform: rotate(45deg);
        }
        .beast::after {
            right: 30px;
            transform: rotate(-45deg);
        }
        /* Leash */
        .leash {
            position: absolute;
            left: 200px;
            bottom: 150px;
            width: 150px;
            height: 2px;
            background-color: #000;
            transform: rotate(-30deg);
        }
        /* Person */
        .person {
            position: absolute;
            left: 330px;
            bottom: 80px;
            width: 50px;
            height: 140px;
            background-color: #000;
        }
        .person::before {
            content: '';
            position: absolute;
            top: -30px;
            left: 15px;
            width: 20px;
            height: 20px;
            background-color: #000;
            border-radius: 50%;
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="header">
            <h1>Taming the Beast</h1>
            <h2>A Guide to OpenAI’s Unpredictable GPTO</h2>
        </div>
        <div class="sections">
            <div class="section">
                <h3>1. Clear Instructions</h3>
                <p>Provide specific and explicit prompts to guide GPTO effectively.</p>
            </div>
            <div class="section">
                <h3>2. Affirmative Directives</h3>
                <p>Use positive language to direct GPTO towards desired outcomes.</p>
            </div>
            <div class="section">
                <h3>3. Break Down Tasks</h3>
                <p>Divide complex requests into smaller, manageable parts.</p>
            </div>
            <div class="section">
                <h3>4. Chain-of-Thought</h3>
                <p>Encourage step-by-step reasoning for complex problem-solving.</p>
            </div>
        </div>
        
        <div class="beast"></div>
        <div class="leash"></div>
        <div class="person"></div>
        <div class="footer">Master prompt engineering to harness GPTO's power.</div>
    </div>
</body>
</html>

Data5 - imageprompts1

1. **Fantastical, Visceral, Action-based:**
   
   "In a surreal, futuristic landscape, a lone figure stands bravely, holding a glowing lasso made of binary code as they attempt to tame a gigantic, swirling beast composed of fragmented texts, audio waveforms, and video frames—each element representing the multifaceted nature of GPTO. The beast’s form is both awe-inspiring and intimidating, its eyes gleaming with unpredictable energy, while the background features towering data streams and floating algorithms. The scene captures the intense struggle and triumph of mastering a powerful, unruly AI."

2. **Split-screen, Realistic:**
   
   "A split-screen image depicting two scenarios side by side: On the left, a realistic portrayal of GPTO incorrectly stating that an elephant is 'the largest rodent in the world,' showing a confused AI interface displaying an elephant labeled as a rodent. On the right, after effective prompt engineering, GPTO correctly identifies the elephant with accurate facts, displaying a confident AI interface with an elephant labeled as the largest land mammal. The contrasting images highlight the transformation from error to accuracy through proper guidance."

3. **Scenic, Photorealistic:**
   
   "Inside a cutting-edge, modern workspace bathed in natural light, a focused prompt engineer sits at a sleek desk surrounded by holographic screens displaying lines of prompt code, data analytics, and AI behavior models. The room is filled with subtle technological elements like floating data points and gentle glowing interfaces. The engineer's thoughtful expression reflects the high-stakes and rewarding nature of prompt engineering. The overall scene exudes professionalism and the forefront of AI innovation."

4. **Realistic, Action-based:**
   
   "A dynamic scene where a person is guiding a humanoid robot through a complex labyrinth constructed of floating words, symbols, and equations. The robot appears perplexed, with question marks visible in its thought bubble, while the guide is pointing the way confidently, holding a scroll labeled 'Refined Prompts.' The maze walls are tall and filled with jumbled text fragments, representing the challenges of unrefined input. The image captures the journey towards clarity and accuracy in AI outputs through human intervention."

5. **Visceral, Fantastical, Photorealistic:**
   
   "An imaginative and slightly surreal image featuring an elephant seamlessly morphing into an oversized rodent within a dream-like landscape. The elephant's form transitions mid-body into that of a beaver, with textures and details so vivid they appear almost tangible. The background is a blurred mix of familiar and abstract elements—trees blending into data clouds, rivers flowing with streaming code—symbolizing the hallucinations of AI like GPTO. The image evokes a sense of wonder and highlights the importance of discernment in AI interpretations."

Article Plan

**1. Suggested Title** - *Taming the Beast: A Guide to OpenAI's Unpredictable GPTO* --- **2. Narrator's Direction** The narrator intends to write an article titled *"Taming the Beast: A Guide to OpenAI's Unpredictable GPTO."* The article aims to: - **Introduce GPTO**: Explain what GPTO-1 is—a new, powerful AI model by OpenAI with advanced chain-of-thought capabilities. It's currently available to GPT Plus members and via API. Despite its strengths in reasoning and planning, GPTO is costly, slow, and in preview. It can produce unpredictable outputs, including hallucinations, making it potentially problematic for tasks like coding. - **Highlight the Challenges**: Emphasize that GPTO's sophistication can lead to convincing yet incorrect outputs, especially if users lack domain-specific knowledge. It requires careful handling and respect. - **Showcase Potential**: Despite its quirks, GPTO has massive potential for high-level planning and ideation, particularly when used with well-crafted prompts. - **Provide Practical Tips**: Offer three "quick wins" or use cases for effectively harnessing GPTO: 1. **Navigate the Fog**: Use GPTO's ability to process large context windows by inputting extensive data (e.g., file trees, research papers) to aid understanding and navigation. 2. **Write Prompts**: Leverage GPTO's exceptional skill in summarizing and refining prompts, enhancing their effectiveness even beyond other AI models like Claude. 3. **Ostentatious Prompts**: Utilize GPTO for complex, high-stakes prompts to generate superior outputs, including visual content. - **Focus on Planning**: Discuss GPTO's strength in planning over execution. Suggest using it for developing product specifications, file structures, and action plans, which can then be implemented using other tools or by developers. - **Look to the Future**: Conclude with thoughts on GPTO's preview status, the upcoming rise of AI agents, and tease future discussions on using GPTO for creating bots and agents. The narrator also notes keywords for research, such as "GPTO specifications" and "coding with Sonnet and Cursor." --- **3. Selected Templates to Follow** To create three distinct drafts, the following formats will be used: 1. **How-To Guide** - *Format*: A step-by-step instructional article guiding readers on effectively using GPTO. - *Structure*: - Introduction to GPTO and its challenges. - Detailed steps on leveraging GPTO's capabilities. - Tips and best practices for prompt engineering. - Conclusion with actionable takeaways. 2. **Listicle** - *Format*: An engaging list-based article highlighting key points. - *Possible Titles*: - "5 Essential Strategies to Master OpenAI's GPTO" - "Top 4 Tips for Taming GPTO's Unpredictability" - *Structure*: - Introduction to pique interest. - Enumerated list of strategies or insights. - Brief explanations for each point. - Conclusion summarizing the benefits. 3. **Opinion Piece** - *Format*: An article expressing the narrator's personal perspective on GPTO. - *Structure*: - Introduction presenting a bold statement or question about GPTO. - Exploration of GPTO's potential and pitfalls. - Personal anecdotes and experiences. - Critical analysis of its impact on AI and coding. - Conclusion with reflections and future outlook. --- **4. Brainstorm Bullet Point Ideas for the Skeleton Structure** **Introduction (10-20%)** - Present GPTO as OpenAI's latest AI breakthrough with immense power and unpredictability. - Use the metaphor of "taming a beast" to captivate readers. - Highlight the dual nature of GPTO: capable yet challenging. - Pose questions about the risks and rewards of using such an advanced model. - Share a brief anecdote about being both amazed and puzzled by GPTO. - Emphasize the article's purpose: to help users harness GPTO effectively. - Mention the increasing relevance of AI in modern tasks. - Address common frustrations faced by early GPTO users. - Set expectations for practical tips and insights ahead. - Create urgency by noting GPTO's rapid development and potential impact. **Middle (60-80%)** - **Part 1: Introducing the Beast** - Detailed overview of GPTO and its origins. - Explanation of chain-of-thought capabilities and how they differ from previous models. - Discussion on accessibility: GPT Plus members and API. - Analysis of GPTO's strengths in reasoning and planning. - Examination of its drawbacks: costliness, slowness, and preview limitations. - Exploration of unpredictability and hallucinations in outputs. - Examples of problematic code outputs (e.g., nonexistent CSS classes, phantom JavaScript functions). - The psychological effect of convincing but incorrect information. - The importance of domain-specific knowledge when using GPTO. - Advice on treating GPTO with caution and respect. - **Part 2: Taming the Chaos – Prompt Engineering with GPTO** - **Tip 1: Navigate the Fog** - Strategies for leveraging large context windows. - Methods for inputting extensive data sets effectively. - Benefits for beginners navigating complex information. - Tips on maximizing limited chat sessions in GPT Plus. - How to switch between GPTO and O1 mini models for efficiency. - **Tip 2: Write Prompts** - Techniques for using GPTO to optimize prompts. - Case studies showing prompt reduction from 1,000 to 100 words. - Comparison with other models like Claude and Anthropics' Prompt Builder. - Insights into improved performance with simplified prompts. - Recommendations for prompting GPTO, O1 mini, and DALL·E. - **Tip 3: Ostentatious Prompts** - Explanation of using elaborate prompts for high-stakes outputs. - Examples of successful outputs like charts and images. - Encouragement to experiment with GPTO's creative potential. - Tips on refining prompts for maximum effect. - **Part 3: Architecting with GPTO – From File Trees to Product Specs** - Illustration of GPTO's exceptional planning abilities. - Step-by-step guide on generating product specifications. - How to instruct GPTO for creating detailed file trees and action plans. - Emphasis on clarity and explicitness in prompts. - Discussion on GPTO's limitations in execution versus planning. - Suggestions for integrating GPTO outputs with tools like Sonnet or GPT-4.0. - Advice on collaborating with developers using GPTO-generated plans. - Highlighting the benefit of GPTO for users without technical expertise. - Potential pitfalls to watch for in the planning process. - Real-world applications and success stories. - **Part 4: The Future of GPTO** - Exploration of GPTO's current preview stage and what it signifies. - Predictions about upcoming AI agents and their implications. - Personal insights from using GPTO for agents and bots. - Discussion on topics to be covered in future articles. - Reflection on the evolving landscape of AI technology. - Encouragement for readers to stay engaged and informed. - Thoughts on GPTO's role in shaping AI's future. **Conclusion (10-20%)** - Summarize key takeaways about GPTO's power and unpredictability. - Reinforce the importance of mastering prompt engineering. - Acknowledge the challenges while highlighting the rewards. - Encourage readers to apply the provided strategies. - Share final reflections on embracing GPTO's potential responsibly. - Pose a thought-provoking statement about the future of AI interactions. - Invite readers to share their experiences or ask questions. - Mention upcoming articles for continued learning. - Offer encouragement to experiment and innovate with GPTO. - Close with a visionary statement on the journey of taming AI. --- **5. Research Keywords** 1. **OpenAI GPTO prompt engineering** 2. **GPTO model limitations and best practices** 3. **Advanced AI planning tools** --- **6. Research News Keywords** 1. **OpenAI GPTO updates** 2. **Latest developments in AI language models** --- **7. Research Questions** 1. What limitations and challenges are associated with OpenAI's GPTO model 2. How does GPTO enhance prompt engineering compared to previous models 3. What effective strategies exist for utilizing GPTO in project planning ---

All URLs Selected

Here are five selected URLs with summaries of the type of information I hope to extract that will enhance your article: -------------------- 1. Comprehensive guide on prompt engineering and strategies to maximize the potential of GPT models. https://platform.openai.com/docs/guides/prompt-engineering 2. Overview of common limitations and challenges of GPT models, providing examples and potential solutions. https://aiperceiver.com/main-limitations-of-gpt-models/ 3. Recent news on OpenAI's GPT models, including updates and new features that may impact planning and ideation capabilities. https://apnews.com/article/openai-gpto-chatgpt-ai-desktop-071dd86594bc310bac07b37cf5e9bafc 4. Business application insights into AI and GPT models, particularly in the realm of project management and advanced planning tools. https://www.hubspot.com/marketing/ai-project-management 5. Detailed analysis of how emerging AI models are enhancing prompt engineering to improve accuracy and application outcomes significantly. https://codingscape.com/blog/26-principles-for-prompt-engineering-to-increase-llm-accuracy -------------------- These URLs should provide a balanced mix of technical details, strategic insights, and recent updates, ensuring the article is both informative and current, especially in relation to GPTO's potential applications and challenges.

All URLs Summary

Certainly! Here's a summary of the content from the crawled data, listing specific pieces of information from each source: -------------------- Source #1: - The guide discusses strategies for improving results from large language models (GPT models like GPT-4o). - Key strategies include writing clear instructions and providing reference text to enhance model responses. - Complex tasks should be broken down into simpler subtasks for better model performance. - Using external tools like text retrieval systems can help compensate for model weaknesses. - Model’s performance improvement necessitates systematic evaluation with reference to standard answers. - Tactics include instructing models to produce outputs of a specific length and adopt a persona for responses. - Use of delimiters and structured outputs can improve the clarity and relevance of model-generated content. - Testing different prompt designs systematically can aid in optimizing model performance. - Techniques like embeddings-based search are suggested for efficient knowledge retrieval by models. - Model safety and accuracy optimization require prompt caching and latency management. -------------------- Source #2: - GPT models excel at mimicking human text but lack genuine understanding. - Main limitations of GPT models include biases, factual inaccuracies, and context awareness. - GPT models depend heavily on their training data, impacting outputs. - Factual inaccuracies pose challenges, as models can't verify data accuracy. - “GPT models can generate convincing yet factually inaccurate statements, such as ‘Elephants are the largest rodents in the world.’” - GPT models’ outputs need human verification, particularly for critical applications. - Regular updates to training data are essential to overcome biases and knowledge gaps. - GPT’s lack of contextual awareness affects output quality, requiring user oversight. - Human supervision is crucial for responsibly utilizing GPT models. - Ajay Rathod emphasizes the importance of understanding AI's potential and limitations. -------------------- Source #3: - OpenAI launched GPTo, enhancing ChatGPT’s text, visual, and audio capabilities. - GPT-4o aims to enable real-time reasoning across text, audio, and video. - OpenAI announced that GPT-4o would be available to the public, including free users. - The update includes emotional tone adjustment and assistance in solving coding problems. - Big players like Google showcase advanced AI capabilities with Gemini 1.5 pro launch. - Former ChatGPT advantages are diminishing as competitors, including Google, catch up. - GPT-4o includes mood detection features from analyzing video inputs. - CEO Sam Altman hints at enhanced AI capabilities reminiscent of the movie "Her." - The enhancements position OpenAI’s AI as capable of more human-like interactions. -------------------- Source #4: - HubSpot’s AI-powered platform includes marketing, sales, and customer service tools. - The HubSpot platform offers various software solutions like CRM and operations hub. - HubSpot emphasizes customer engagement and easy management of content. - Users can connect their favorite apps to HubSpot's platform to enhance productivity. - HubSpot's AI features aim to improve lead generation and customer service efficiency. - Integration with external apps simplifies HubSpot user workflows. - HubSpot's platforms come with free and premium plans tailored for startups and enterprises. - HubSpot’s AI is incorporated throughout its platform, enhancing user interaction. -------------------- Source #5: - Prompt engineering improves language model accuracy significantly (up to 57% on LLaMA, 67% on GPT-3.5/4). - "Prompt engineering is being recognized as a high-paying skill, with positions offering $250-375k per year." - The 26 principles of prompt engineering include techniques like role assignment and step-by-step guidance. - Techniques for improved model accuracy include using "Chain-of-thought" combined with "few-shot" prompts. - Engaging prompts involve clear structure, audience integration, and affirmative directives. - “Give explicit task instructions like ‘do’ and avoid using negative language like ‘don’t’.” - The principles suggest leveraging audience-specific language to tailor model responses. - Employ example-driven and few-shot prompting for higher model output accuracy. - Encouragement of breaking complex tasks into simpler prompts for model clarity. - The research highlighted methods to boost AI interaction effectiveness through prompt design. -------------------- This summary captures key factual statements and relevant information from the sources, focusing on aspects that align with the potential content of your intended essay on OpenAI's GPTO and related AI models.

Final Article Structure

Certainly! Based on your initial transcription, the article plan, the research summary, and the guidelines provided, here's the updated skeleton structure for your article: --- **Title:** *Taming the Beast: A Guide to OpenAI's Unpredictable GPTO* --- ### **Introduction (2-5 bullet points)** - **Present GPTO as OpenAI's Latest Breakthrough:** - Introduce GPTO as a powerful new AI model from OpenAI, boasting advanced chain-of-thought capabilities that push the boundaries of AI technology. - **Use the Metaphor of "Taming a Beast":** - Captivate readers with the idea that harnessing GPTO is akin to taming a wild, unpredictable beast. - **Highlight the High Stakes of Prompt Engineering:** - Mention that prompt engineering is now a highly sought-after skill, with positions offering between **$250,000 to $375,000 per year** (Source #5). - **Pose Engaging Questions to the Reader:** - Ask if readers are prepared to navigate the challenges and reap the rewards of utilizing such an advanced yet unpredictable AI tool. - **Set the Scene with a Personal Anecdote:** - Share a brief story about being both amazed and perplexed by GPTO's outputs, emphasizing its dual nature of brilliance and unpredictability. --- ### **Middle Section (5-12 bullet points)** #### **Part 1: Introducing GPTO – The Beast Unleashed** - **Explain What GPTO Is:** - Detail GPTO's capabilities, including its advanced chain-of-thought reasoning and ability to handle complex, multi-modal inputs (text, visual, and audio) (Source #3). - **Accessibility and Current Limitations:** - Note that GPTO is available to GPT Plus members and via API, but is currently in preview, making it costly and sometimes slow. - **Unpredictability and Challenges:** - Discuss GPTO's tendency to produce unpredictable outputs, including hallucinations and factual inaccuracies, which can be problematic in critical applications (Sources #1 & #2). - **Examples of Missteps:** - Provide examples such as GPTO generating code with nonexistent classes or making factual errors like claiming **“Elephants are the largest rodents in the world”** (Source #2). - **Importance of Domain Knowledge:** - Emphasize that users lacking specific domain expertise may find it challenging to detect GPTO's convincing yet incorrect outputs (Source #2). #### **Part 2: Taming the Beast – Mastering Prompt Engineering** - **The Rise of Prompt Engineering:** - Highlight that prompt engineering can improve model accuracy by up to **57% on LLaMA and 67% on GPT-3.5/4** (Source #5). - **Lucrative Career Opportunities:** - Note that prompt engineering is recognized as a high-paying skill, with salaries ranging from **$250,000 to $375,000 per year** (Source #5). - **Essential Strategies:** - **Write Clear Instructions:** - Stress the importance of clarity and specificity in prompts to enhance model responses (Source #1). - **Use Explicit Task Directives:** - Recommend using affirmative directives and avoiding negative language to improve output quality (Source #5). - **Break Down Complex Tasks:** - Advise on decomposing complex requests into simpler subtasks for better performance (Sources #1 & #5). - **Leverage "Chain-of-Thought" and "Few-Shot" Prompts:** - Explain how these techniques can significantly enhance accuracy and reasoning (Source #5). - **Applying the 26 Principles of Prompt Engineering:** - Discuss techniques such as role assignment, step-by-step guidance, and structured outputs to optimize GPTO's performance (Source #5). - **Case Study – Reducing Prompt Length:** - Share an example of using GPTO to condense a **1,000-word prompt to 100 words** without losing effectiveness, showcasing its summarization skills. #### **Part 3: Harnessing GPTO's Potential – From Planning to Execution** - **GPTO's Strength in High-Level Planning:** - Illustrate how GPTO excels in tasks like generating product specifications, designing file structures, and outlining action plans. - **Navigate the Fog with Large Context Windows:** - Explain how users can input extensive data (e.g., file trees, research papers) into GPTO, leveraging its large context window to comprehend and organize complex information (Article Plan). - **Writing Effective Prompts:** - Emphasize GPTO's ability to refine and optimize prompts for other AI models, potentially outperforming tools like Claude (Article Plan). - **Ostentatious Prompts for Superior Outputs:** - Encourage users to experiment with elaborate prompts to produce high-quality outputs, including visuals and detailed plans. - **Integration with Other Tools:** - Suggest combining GPTO-generated plans with execution tools or collaborating with developers to bring ideas to fruition (Article Plan). - **Human Oversight is Crucial:** - Reinforce that despite GPTO's capabilities, human verification is essential to ensure accuracy, especially for critical applications (Sources #2 & #4). #### **Part 4: Looking Ahead – The Future of GPTO and AI Technology** - **GPTO's Preview Status and Impending Developments:** - Discuss GPTO's current state and the anticipation surrounding its full release to the public, including free users (Source #3). - **Advancements in AI by Industry Leaders:** - Mention competitors like Google's launch of **Gemini 1.5 Pro**, highlighting the rapid advancements in AI technology (Source #3). - **Real-Time Multimodal Reasoning:** - Explore how GPTO aims to enable real-time reasoning across text, audio, and video, enhancing emotional tone adjustment and problem-solving capabilities (Source #3). - **AI Agents and Enhanced Interactions:** - Reflect on CEO **Sam Altman's** hints at AI capabilities reminiscent of the movie **"Her"**, pointing toward more human-like interactions (Source #3). - **Staying Informed and Engaged:** - Encourage readers to keep abreast of AI developments, as tools like GPTO will significantly impact various industries and workflows. --- ### **Conclusion (2-5 bullet points)** - **Summarize GPTO's Dual Nature:** - Recap the immense potential and unpredictability of GPTO, reiterating the metaphor of taming a wild beast. - **Reinforce the Importance of Prompt Mastery:** - Emphasize that mastering prompt engineering is key to harnessing GPTO effectively, potentially improving accuracy by up to **67%** (Source #5). - **Acknowledge the Challenges and Rewards:** - Recognize the hurdles users may face but highlight the significant benefits of leveraging GPTO for planning and innovation. - **Encourage Exploration and Innovation:** - Motivate readers to apply the strategies discussed, experiment with GPTO's capabilities, and contribute to shaping the future of AI technology. - **Pose a Thought-Provoking Future Outlook:** - Leave readers with a contemplative question about the evolving relationship between humans and AI, inspired by the vision of more human-like AI interactions. --- This updated skeleton structure integrates key ideas from your initial transcription and article plan while incorporating specific facts, data, and quotes from the research summary. It includes: - The high demand and lucrative salaries for prompt engineers, offering **$250,000 to $375,000 per year** (Source #5). - Significant improvements in model accuracy through prompt engineering—up to **57%** on LLaMA and **67%** on GPT-3.5/4 (Source #5). - Examples of GPTO's limitations, such as generating convincing yet factually incorrect statements like **“Elephants are the largest rodents in the world”** (Source #2). - The mention of OpenAI's plans to make GPTO available to the public, including free users, and the enhancements in emotional tone adjustment and coding assistance (Source #3). - References to competitors' advancements, such as Google's **Gemini 1.5 Pro** (Source #3). - Emphasis on human oversight being crucial when working with AI models (Source #2). By following this structure, you can create a comprehensive and engaging article that not only informs readers about GPTO but also provides practical insights and encourages thoughtful consideration of AI's future impact.

Chart.js 1

Average Annual Salaries for Tech Roles

Table

Pros and Cons of GPTO

Pros	Cons
Advanced reasoning abilities Multimodal understanding (text, audio, video) Handles complex tasks effectively Excels in planning and brainstorming High-level content summarization Potential to reshape industries High demand for prompt engineers with lucrative salaries	Unpredictable outputs without proper guidance Prone to hallucinating facts Expensive and limited access Requires careful prompt engineering Needs human oversight to spot errors May generate gibberish if not properly guided Competition from other AI models like Gemini 1.5 Pro

```

SPA

Taming the Beast: A Guide to OpenAI’s Unpredictable GPTO

1. Clear Instructions

Provide specific and explicit prompts to guide GPTO effectively.

2. Affirmative Directives

Use positive language to direct GPTO towards desired outcomes.

3. Break Down Tasks

Divide complex requests into smaller, manageable parts.

4. Chain-of-Thought

Encourage step-by-step reasoning for complex problem-solving.