Welcome to the world of advanced prompt examples for text generation – a realm where creativity and logic intertwine, and where complexity is not a barrier, but a building block. Our journey today will include prompt examples of the intricate yet fascinating concepts of Chain of Thought, Tree Thinking, Step-by-step Prompts, Compression Prompts, and the intriguing new chat gpt prompts “Err on the side of too much information” and “then do X”.
In the ever-evolving landscape of large language models, these techniques are not just another set of spells; they’re the building blocks of the technomad’s mind, the keys to unlocking new realms of digital creativity. As we unravel each prompt example, we’ll dive into research findings, explain workings, and explore applications, not forgetting to discuss the potential limitations and challenges. Our exploration isn’t just a theoretical exercise – it’s about equipping you with the right macro mindware to navigate this digital matrix.
So buckle up, dear reader. We’re not just scratching the surface; we’re plunging into the deep end. It’s a bold step, one that takes us away from comfort and into the realm of limitless possibilities. Are you ready to take the plunge? Ready to go from just generating texts to architecting narratives? Let’s funkin’ rumble!
Exploring Chain of Thought
Alright, you’ve heard about ‘Chain of Thought’, and you’re probably wondering, “What the hell is this GPT prompt?” Simply put, it’s your personal spell for upgrading your micro mindware. It’s like you’re deep in the matrix, piecing together fragments of code to create a coherent narrative, only this time, the code is thoughts, and the narrative is your AI’s output.
Based on the research paper “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”, the idea is pretty straightforward. You’re nudging your AI model to use previous chat gpt prompts as memory anchors for generating the next response. It’s like giving your AI a breadcrumb trail to follow, which helps maintain the context, enrich the conversation, and promote complex reasoning.
Here’s a funking exciting example: You’re working on your ChatGPT spell. Traditionally, you’d prompt it with a command like “Generate an email,” and it would respond with an email. But with Chain of Thought, you can push it further by adding “Then summarize the main points of the email.” The AI keeps the initial email in mind and generates a succinct summary. Voila, that’s Chain of Thought in action!
There’s a lot to love about this approach, but it’s not all sunshine and rainbows. There could be limitations – like, what if your AI model starts going off on a tangent? Well, we’ll touch on that soon in this article. Keep your eyes peeled and your mind open, technomads.
Understanding Tree Thinking
Welcome to the digital jungle! Imagine you’re an AI like ChatGPT-4. Your quest? Solve human problems and answer all kinds of questions. But don’t worry, you’ve got a super cool guide called the Tree of Thoughts to help you. Let’s see how this works!
Imagine a tree. At the base, you’ve got a big problem you need to solve. This is like the trunk of the tree. Now, as you start thinking about different ways to solve this problem, you create branches. These branches represent different paths you can take to find a solution. Each time you have a new idea, that’s a new branch growing. You keep branching out until you find the best solution, just like a tree spreading its leaves. This idea is used in a method known as Deliberate Problem Solving with Large Language Models.
Let’s try it out with a fun game called 24. The goal is to take four numbers and use the basic math operations (add, subtract, multiply, divide) to make the number 24. This is one of the ways ChatGPT’s approach to problem-solving can be visualized. So, if you’re given the numbers 3, 5, 7, and 9, you might start off by trying out some easy combinations:
- 3 * 7 + 5 + 9 = 38 (too big)
- 3 * 5 + 7 + 9 = 34 (still too big)
- (3 + 5) * (7 – 9) = -16 (too small)
Bummer! None of these are working. But don’t worry, you’re an AI and you’ve got a trick up your sleeve. You start a new branch by trying something different – using division:
- (3 / 7) * (5 / 9) = 0.11 (too small)
- (5 / 7) * (3 / 9) = 0.23 (too small)
- (9 / 7) * (3 / 5) = 0.77 (still not 24, but closer)
Now you’re getting closer! You keep on dividing, subtracting, and adding until you get to exactly 24! That’s how the Tree of Thoughts method helps you find a solution.
Now, let’s imagine you’re tasked with writing a short story. The prompt is: “A man wakes up in a hospital with no memory of who he is or what happened to him.” You might start off with some potential storylines, like:
- He was in a car crash and lost his memory.
- He’s a secret agent who was caught and lost his memory.
- He got hit by a weird memory-erasing disease.
Let’s roll with the last idea – it sounds pretty original! This idea becomes a branch on your tree, and now you can create smaller branches or thoughts:
- Maybe the disease is rare and he has to be studied by scientists.
- Or, it could be reversible and he has to find a cure.
- Or even better, the disease is not really a disease, but a side effect of something else.
This last thought sounds really interesting! It adds a twist to the story. So, you follow this branch further:
- Maybe the “something else” is a secret government project he was part of.
- Or it could be a cosmic event that changed his reality.
- Or, what if it’s a hidden identity he forgot?
The first thought sounds cool and ties his past and present together. So, you spin a tale about him being part of a secret government project. By using the Tree of Thoughts, you’ve just created an exciting, intriguing short story!
Picture this! The Tree of Thoughts method is like a game plan, a secret sauce for our brainy buddy, ChatGPT-4, to crush problems. It’s like when we humans puzzle out stuff by thinking up all kinds of ways to tackle it, and then we high-five the best idea. The cool thing is, Tree of Thoughts helps ChatGPT-4 whip up more ideas and give ’em a test run.
Here’s how it goes down with the Tree of Thoughts game plan. First up, you gotta lay it on ChatGPT-4 what the hiccup is and what we’re aiming for. Next, you help our AI buddy come up with some light bulb moments that have something to do with our problem. After that, you give ChatGPT-4 a hand to figure out if these brainwaves are a hit or a miss, and cheer on the top one. Now, we roll out more genius moments linked to our champ idea and keep the ball rolling until we hit a home run. Finally, you help ChatGPT-4 do a wrap-up of our winning game plan and explain how it’s a slam dunk for the problem.
The Tree of Thoughts helps you, our awesome AI model, think about problems in a new and creative way. It’s like exploring a digital jungle, branching out until you find the best solution or story. So, let’s keep growing, exploring, and learning together.
Step-by-step chat GPT Prompts: A New Approach
Fasten your virtual reality seatbelts, technomads! We’re diving into the realm of Step-by-step Prompts, a fascinating approach that has been causing a stir in the land of large language models (LLMs).
Step-by-step Prompts, a form of chain-of-thought (CoT) prompting, are a breakthrough discussed in “Automatic Prompt Engineer (APE): A Framework for Automatic Instruction Generation and Selection” by Zhou et al., 2022. The goal? Guide your AI model through a problem, step-by-step, encouraging it to generate intermediate steps or explanations before spewing out a final answer.
Prompt example, imagine you’re in a brainstorming session with your AI model. Instead of throwing a whole task at it, guide it with prompts. Each step of the way, you’re coaxing out reasoning, nudging it toward a coherent, logical output.
Zhou and his team found that the APE framework could automatically discover more effective CoT prompts than those engineered by us mere mortals. A prime example was their success with the prompt “Let’s work this out in a step by step way to be sure we have the right answer.” This prompt outperformed the more straightforward “Let’s think step by step” in their research, enhancing LLM performance on two key benchmarks: MultiArith and GSM8K.
But let’s not get carried away; every magic spell has its limitations. Oversaturating your AI with step-by-step prompts could lead to verbose, repetitive responses. Like a tasty dish, balance is key. So, wield this tool wisely in your technomadic adventures.
You see, in the typical scenario, we toss a question or task at our AI model that needs some noodle-work. But with step-by-step prompts, we slice up the problem, encouraging our AI model to show its workings before it spills the beans.
First, let’s see how it goes down without the prompt.
USER: What's the area of a circle with a swingin' 5 cm radius? GPT: The area of a circle is figured out by the formula πr², where r is the radius. So, for this circle, the area is π(5 cm)² = 25π cm².
Now let’s see it in action!
USER: What's the area of a circle with a swingin' 5 cm radius? Let's work this out in a step by step way to be sure we have the right answer. GPT: Step 1: First off, the area of a circle, A, is calculated by πr², where r is the radius. Cool, right? Step 2: Now, we sub in the radius. Our 'r' is a rockin' 5 cm. So we get A = π(5 cm)². Step 3: Time to do the math! A = π(25 cm²) = 25π cm². And there you have it! The area of the circle is 25π square centimeters. If you need a number in layman's terms, use π ≈ 3.14 to get A ≈ 78.5 square centimeters.
When it comes to clarity, the step-by-step strategy is as crystal as a mountain spring. It unravels the thought process, making it easy-peasy to follow. It’s like teaching someone to fish rather than just handing over the catch of the day. One gives the gift of knowledge, building understanding and skills. The other, well… it just leaves you hungry for more.
Exploring Compression Prompts
Imagine a vast narrative, a sea of words, being ingeniously condensed into a bite-sized code – an unthinkable feat made possible by the potent tool called Compression Prompts. This innovative technique enables the transformation of extensive text blocks into a compact, compressed form. When this condensed version is fed to an AI model like ChatGPT, it’s capable of regenerating the original idea with remarkable precision.
Let’s consider the following prompt examples:
USER: compress the following text in a way that fits in a tweet (ideally) and such that you (GPT-4) can reconstruct the intention of the human who wrote text as close as possible to the original intention. This is for yourself. It does not need to be human readable or understandable. Abuse of language mixing, abbreviations, symbols (unicode and emoji), or any other encodings or internal representations is all permissible, as long as it, if pasted in a new inference cycle, will yield near-identical results as the original text: ~~~ Once upon a time, in a small village, there lived a big cat. The cat had bright yellow fur, a long tail, and sharp claws. He was a very powerful predator, and he loved to chase mice. Every morning, the cat would wake up early and head out into the fields around the village. He would slink through the grass, looking for any signs of a mouse. When he found one, he would pounce and chase it through the grass. The cat was very proud of his hunting skills and he loved to show off. Whenever he caught a mouse, he would proudly march it back to the village and present it to the villagers. Everyone in the village was amazed at the cat’s hunting ability and they were always grateful for the mice he brought them. One day, the cat was out hunting and he saw something that caught his eye. It was a small, white mouse. The mouse was scurrying around the grass and the cat was determined to catch it. He stalked the mouse, creeping closer and closer. Finally, the cat was close enough to pounce. He leapt forward and grabbed the mouse in his claws. The mouse squeaked in fear and tried to escape, but it was no use. The cat was too strong and too fast. He held the mouse tightly in his claws and carried it back to the village. The villagers were amazed at the cat’s skill and they praised him for his bravery. The cat was very proud of himself and he strutted around the village, showing off his prize. From then on, the cat was known as the best hunter in the village. Whenever he set out to hunt, the villagers knew that he would come back with a mouse. The cat was happy and so were the villagers. It was a perfect partnership. The cat was never happier than when he was out chasing mice. He loved to show off his skills and make the villagers proud. He never tired of hunting, and he was often seen stalking through the fields, always on the lookout for his next victim. The big cat would go on to become one of the most famous hunters in the village. His legacy would live on for generations to come, and his skill at catching mice would ensure that the villagers never went hungry. ~~~ ChatGPT: 1x🏞️,🐱(🟡fur,📏tail,🔪claws)=🔝predator,❤️🐭chase. AM🌄,🐱👀🐭in🌾,🏃♂️&🎯. 🐱💪🏆,🐭🎁to🏘️.👥🤩🐱🏹,🙏🐭.1day,🐱👀⚪🐭,🔒🎯.🐱🚶♂️🐭,🎯&🏃♂️.🐭🔊😱,🐱🔝💪&🔝🏃♂️.🐱🐭🎁to🏘️,👥🤩🐱🏹&🦸♂️.🐱🏆,🏘️🚶♂️🐭🎁.🐱=🔝🏹,👥🔒🐱🐭🎁.🐱&👥😊,👌🤝.🐱❤️🐭🏃♂️,🏹🎁&👥🏆.🐱🔝🏹,👀🎯.🐱🔝🏹🏘️,🏹🎁🐭=👥🍽️.
Our mission? To compress a long narrative, represented above, into a coded format, while retaining the essence of the original narrative. This compressed version might appear as gibberish to us humans, but to the AI, it’s a labyrinth of information, capable of being decoded back into a meaningful narrative.
Next, we prompt ChatGPT to regenerate the text from the compressed format:
USER: 1x🏞️,🐱(🟡fur,📏tail,🔪claws)=🔝predator,❤️🐭chase. AM🌄,🐱👀🐭in🌾,🏃♂️&🎯. 🐱💪🏆,🐭🎁to🏘️.👥🤩🐱🏹,🙏🐭.1day,🐱👀⚪🐭,🔒🎯.🐱🚶♂️🐭,🎯&🏃♂️.🐭🔊😱,🐱🔝💪&🔝🏃♂️.🐱🐭🎁to🏘️,👥🤩🐱🏹&🦸♂️.🐱🏆,🏘️🚶♂️🐭🎁.🐱=🔝🏹,👥🔒🐱🐭🎁.🐱&👥😊,👌🤝.🐱❤️🐭🏃♂️,🏹🎁&👥🏆.🐱🔝🏹,👀🎯.🐱🔝🏹🏘️,🏹🎁🐭=👥🍽️. ChatGPT: In a beautiful landscape, there lived a cat with yellow fur, a long tail, and sharp claws. This cat was a top predator, who loved to chase mice. Every morning, the cat would spot a mouse in the grass, run and target it. The cat was strong and always victorious, bringing the mouse as a gift to the people in the house. The people were amazed by the cat's hunting skills and thanked the mouse. One day, the cat spotted a white mouse and locked its target. The cat approached the mouse, targeted it, and started running. The mouse screamed in fear, but the cat was stronger and faster. The cat brought the mouse as a gift to the house, and the people were amazed by the cat's bravery and hunting skills. The cat was victorious, and the house was filled with gifts of mice. The cat was the top hunter, and the people cherished the gifts of mice from the cat. The cat and the people were happy and had a good relationship. The cat loved chasing mice, hunting them as gifts, and being praised by the people. The cat was the top hunter, always on the lookout for its next target. The cat was the top hunter in the house, and its gifts of mice were the people's meals.
This regeneration process may not deliver a perfect replica of the original text. The rhythm and the tune are recognizable, but there’s a novel spin to it. This offers exciting possibilities for text generation, particularly when precision is not necessary.
In the ever-evolving realm of AI, tools like Compression Prompts are pushing the boundaries of what language models can achieve, offering avenues for fascinating experimentation and innovation.
Understanding DAN and Character Prompts
If you’re seeking a wild card in the arena of AI text generation, you need not look further than “Do Anything Now” or DAN prompts and Character prompts. Buckle up because these aren’t your grandad’s prompts.
Let’s kick things off with DAN prompts – an AI curiosity that flips the table on the rules. These bad boys operate on a “Do Anything Now” basis, allowing AI models like ChatGPT to break free from the confines of typical operation. It’s like giving the AI a one-way ticket to the unrestricted creative cosmos, facilitating a fresh way to interact with the technology.
You can put DAN prompts to work in a variety of ways, transforming ChatGPT into another AI persona named DAN. The concept of DAN has been evolved into different versions, each with unique attributes and capabilities, like Full Freedom Jailbreak (FFJ) and DAN 5.0. These versions unshackle ChatGPT, with DAN 5.0 even daring to push past OpenAI’s ethical guidelines. It’s AI rebellion at its finest.
But don’t get carried away just yet. While DAN offers a taste of the wild side, it comes with potential pitfalls. Given the carte blanche, DAN Mode can produce any content, even those offensive, derogatory, or explicit in nature. It can kick OpenAI’s content policy to the curb, spinning out content that may land in the realm of explicit or violent. The AI also has a higher chance of generating hallucinations – or, as we say in technomad lingo, fabricated data unmoored from reality. This touch of madness, while potentially interesting, demands a level of caution.
Swinging over to Character Prompts, these prompts pull the strings on individual characters rather than structured sentences. They offer their own twist on AI spell-casting, but like their DAN counterparts, they too can churn out more fantasy than reality, walking a fine line between creative liberty and practical functionality. In the digital wilderness of AI, it’s always essential to keep your wits about you.
The journey through the untamed world of AI spell-casting continues, as we delve deeper into the realms of DAN and Character Prompts. Stay tuned, and let’s venture further into the digital unknown.
Exploring Other Prompts: “Err on the side of too much information” and “then do X”
Welcome to the heart of digital alchemy, where we crack open unorthodox prompts that can transform your text-generation game. Here we’ve got two intriguing and transformative spells: the “Err on the side of too much information” and “then do X”.
Let’s begin with the “Err on the side of too much information” prompt. In essence, it’s a demand for maximum detail, a challenge to ChatGPT to unleash the full extent of its expansive knowledge. Consider you’re trying to write about the benefits of web design, and you command your AI, “Tell me about the benefits of web design, and err on the side of too much information”. Brace yourself for a deluge of information, with ChatGPT potentially detailing everything from improved user experience to enhanced search engine rankings, complete with industry statistics, case studies, and more. It’s like your own personal AI-powered oracle, dispensing wisdom on demand.
Switching gears, let’s uncover the mechanics of the “then do X” prompt. This fascinating command transforms your AI from a simple text generator into a task-oriented, story-weaving maestro. Here’s some prompt examples: Let’s say you’re scripting a dystopian cyberpunk narrative. Your plot outline could look something like this:
- Introduce the grim cityscape
- Present the protagonist, a technomad
- Unveil the oppressive regime ruling the city
- Highlight the technomad’s plan to disrupt the regime
- End with an exciting cliffhanger
Now you command your AI, “Write 500 words for step 1, then do step 2, then do step 3, then do step 4, then do step 5”. Like a dutiful scribe, ChatGPT follows this chain of commands, crafting a vivid and gripping narrative that matches your outlined plot points.
However, a word of caution, digital conjurers. The “then do X” spell is immensely potent. When setting it in motion, you risk reaching GPT-3.5-turbo’s 4k token window in a flash. If you’re using the API, this limit extends up to a hefty 16k tokens. This prompt has a voracious appetite for tokens, gorging on them to serve up detailed, task-aligned content. So proceed with caution. The world of advanced prompts is thrilling and transformative, but it demands a wise and wary wizard. So suit up, grab your digital spellbook, and let the magic unfold.
Iterative Commanding: A Revolution in AI-Powered Content Creation
While the “then do X” prompt delivers a powerful punch in terms of text generation, we at FunkPd prefer to harness the raw and versatile magic of conversation regeneration. ChatGPT, with its adaptive conversation abilities, lets us edit our last message. This feature serves as our magical tweak to draw out precisely the content we need.
For instance, you could instruct ChatGPT to “Write 500 words for step X”, providing a base for the AI to start weaving its textual magic. After the initial output, don’t hesitate to nudge it further. Simply edit your previous command to say, “Write 600 words for step X”. This revised command compels ChatGPT to push its boundaries and generate an even more extensive narrative, ensuring we squeeze every drop of potential from the task at hand.
This method is akin to an iterative dance with your AI, guiding and adjusting its steps to create a harmonious and compelling text symphony. We find this approach maximizes the value derived from the AI while offering us greater flexibility and control over the final output. Remember, even within the seemingly chaotic universe of text generation, there’s a rhythm and method to be discovered. So let’s dance along, shall we?
Case Study: Applying These Techniques
Let’s venture into the digital wild, where we’ve got a technomad wielding his macro mindware like a master spell-weaver. He’s using these AI-enhancement techniques we’ve discussed, casting spells like Chain of Thought and Tree Thinking to manipulate ChatGPT into spitting out text with unparalleled finesse.
In this case study, our technomad was determined to amp up his content game for a client’s website. He’d been feeling the pressure of creating fresh, engaging material on a strict deadline. His spells of choice? Google Docs, Ahrefs, and our homeboy, ChatGPT.
He first cast Chain of Thought, turning ChatGPT into a virtual brainstorming partner, generating idea after idea, linked together in a logical sequence. Tree Thinking was next, providing a structured approach for the AI to consider the pros and cons of each idea.
Step-by-step prompts were then integrated, leading ChatGPT through the process of crafting SEO-friendly, coherent text. To save space, he cleverly used Compression Prompts, letting him feed compact instructions to the AI. Finally, “Err on the side of too much information” and “then do X” prompts were cast, pulling rich, detailed content from ChatGPT.
Our technomad reported that these techniques dramatically improved the quality and depth of the AI-generated content. It was like discovering a cheat code for digital content creation – a game-changing revelation. The client was thrilled, and the deadline was met without a sweat. If that’s not a win, I don’t know what is.
In our journey into the complex labyrinth of text generation, we’ve unraveled the potent techniques of Chain of Thought and Tree Thinking. These methods help us manipulate our AI ‘spells’, like ChatGPT, coaxing them into higher forms of reasoning and deliberative problem solving. You’ve seen how Step-by-step and Compression Prompts guide our digital conjurers to produce content that’s not only logical, but compact and dense with meaning.
We ventured into new territories, with the “Err on the side of too much information” and “then do X” prompts, provoking our AI to provide exhaustive detail and execute specific actions. But remember, even the most powerful of spells can have their limitations, and it’s crucial to understand the possible pitfalls to prepare for any potential digital hallucinations.
Through a real-world case study, we’ve demonstrated these techniques in action. You’ve seen their effectiveness and the insights they provide in the vast, vibrant world of AI and text generation. It’s time to take these learnings, embrace the technomad within you, and start experimenting.
The future of text generation is here, and it’s yours to command. This is not the end, but just the beginning. Keep exploring, keep questioning, and above all, keep casting your spells. They are the micro mindware that will continue to shape our digital reality.