The Official ChatGTP cheat code thread.

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[Westbama Heartthrob]

Subs

 

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Used it as a personal therapist to talk through my thoughts

 

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people been using it for that since day 1, theres money in it.

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Today Sam Altman and Arianna Huffington announced the launch of Thrive AI Health, a new company whose mission is to use AI to democratize access to expert-level health coaching to improve health outcomes. Read more in @TimE: AI-Driven Behavior Change Could Transform Health Care

2/5
Love to see more builders in healthcare AI!

3/5
Sus tweet, Deepak.

4/5
@ariannahuff is bringing her master plan to life (again)!

5/5
We built a company to do that almost 1 year ago!

THRIVEbyAI | LinkedIn


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Today Sam Altman and I published a piece in TIME sharing our vision for how AI-driven personalized behavior change can transform healthcare and announcing the launch of Thrive AI Health, a new company funded by the OpenAI Startup Fund and Thrive Global, which will be devoted to building an AI health coach. The company’s mission is to use AI to democratize access to expert-level health coaching to improve health outcomes and address growing health inequities.

As @sama and I write, AI could go well beyond efficiency and optimization to something much more fundamental: improving both our health spans and lifespans.

With AI-driven personalized behavior change, we have the chance to finally reverse the trend lines on chronic diseases like diabetes and cardiovascular diseases, which are directly related to daily behaviors but not distributed equally across demographics.

DeCarlos Love — a brilliant product leader passionate about improving health outcomes — has left Google to become Thrive AI Health’s CEO, and I’m very much looking forward to working with him. And The Alice L. Walton Foundation is joining us as a strategic investor to help us scale our impact to underserved communities and reduce health inequities.

AI has become central to @Thrive's mission to improve health and productivity outcomes, and I’m incredibly passionate about the opportunity to leverage AI to deliver hyper-personalized behavior change across the five key behaviors that Thrive focuses on and that govern our health: sleep, food, movement, stress management and connection. The AI health coach will be embedded in Thrive’s behavior change platform and we look forward to bringing this innovative offering to the market.

Read more in
@TimE
: AI-Driven Behavior Change Could Transform Health Care


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1/2
BREAKING NEWS

OpenAI CEO Sam Altman and Thrive Global’s Arianna Huffington launch a health coach company

Meet Thrive AI Health

It will use artificial intelligence and help people take charge of their well-being and make expert health advice accessible to all.

As per them, AI could help improve “both our health spans and our lifespans”.

Thrive AI Health will primarily focus on promoting healthy behaviour, like: getting enough sleep, eating well, exercising, spending time in nature and meditating. Furthermore, this AI health coach wouldn't just tell you to eat better or exercise more, but promises to take your preferences, your schedule and your health data into account.

2/2
Courtesy: BI India


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[bnew]

Maximizing the Utility of Large Language Models (LLMs) through Prompting

In this article, authors Numa Dhamani and Maggie Engler discuss how prompt engineering techniques can help use the large language models (LLMs) more effectively to achieve better results. Prompting techniques discussed include few-shot, chain-of-thought, self-consistency, and tree-of-thoughts...

www.infoq.com

Maximizing the Utility of Large Language Models (LLMs) through Prompting​

JAN 19, 2024 14 MIN READ

by Numa Dhamani

Principal Machine Learning Engineer @Kungfu

Maggie Engler

Engineer & Researcher on LLMs @Inflection

Key Takeaways​

• Prompt engineering is about experimenting with changes in prompts to understand their impacts on what large language models (LLMs) generate as the output. Prompt engineering yields better outcomes for LLM use with a few basic techniques
  
• Zero-shot prompting is when an LLM is given a task, via prompt, for which the model has not previously seen data
  
• For the language tasks in the literature, performance improves with a few examples, this is known as few-shot prompting
  
• Chain-of-Thought (CoT) prompting breaks down multi-step problems into intermediate steps allowing LLMs to tackle complex reasoning that can't be solved with zero-shot or few-shot prompting
  
• Built upon CoT, self-consistency prompting is an advanced prompting technique, that provides the LLM with multiple, diverse reasoning paths and then selects the most consistent answer among the generated responses
  
  
  
  

A new job title, "prompt engineer," has made waves in tech media recently, bursting onto the careers pages of top AI companies with promises of eye-wateringly high salaries. But what even is prompt engineering? The term itself was coined only within the past few years and refers to the art and science of prompting large language models (LLMs) to achieve better results.

These LLMs, which include the popular ChatGPT and GPT4 from OpenAI, Claude from Anthropic, and Bard from Google, can be used for all types of language tasks, from drafting emails and documents to summarizing or classifying text.

Prompting, essentially, refers to the practice of giving inputs to the LLMs, typically a set of instructions describing how to perform the desired task. For example, to use ChatGPT to draft a cover letter based on a job description, one might use the prompt,

"You are an applicant applying for the following job. Write a cover letter explaining why you are well-suited for the role."

Easy enough, right? Here's where it gets tricky: researchers have found that the results provided by LLMs depend heavily on the specific prompt given, and so even though it might seem trivial to explain a well-defined task like writing a cover letter, simple adjustments like wording and formatting changes can substantially affect the output received.

Prompt engineering is fundamentally experimenting with changes in prompts to understand their impacts on what the model generates, and therefore doesn't require an advanced technical background - merely a little curiosity and creativity.

Furthermore, prompt engineering is not just the purview of highly paid programmers. We argue that everyone who uses LLMs can, and should, be a prompt engineer. The most basic reason is that prompt engineering will yield better outcomes for LLM use, and with even a few basic techniques, it's possible to measurably improve performance on many common tasks.

Few-Shot Prompting​

Let's consider a task for which someone might want to employ an LLM: classifying customer reviews by sentiment. When an LLM is given a task, via prompt, for which the model has not previously seen data, this is referred to as zero-shot prompting. In this case, the task would be a zero-shot classification task. The zero-shot prompt might be something like the figure shown below, where the user gives instructions on classifying movie reviews to Google's Bard.

This might work reasonably well on its own, but across a variety of language tasks in the literature, performance nearly always improves with a few examples, or a few-shot prompt. In this case, the few-shot prompt might start the same way, but append several examples of the expected output. Again, we see that Bard generates a response of "POSITIVE" for the final unlabeled review.

More precisely, this prompt would be two-shot, referring to the fact that there are two examples provided to the model. The fact that providing examples in this way measurably improves the model's ability to complete the task is unique to LLMs; as a result, unlike other machine learning models, LLMs are able to perform a wide variety of tasks that they are not trained to do.

However, every approach comes with its own limitations. While few-shot prompting is an effective technique for numerous tasks, it tends to fall short when addressing more complex reasoning tasks. Let’s look at an example. In the figure below, we provided ChatGPT with a multi-step reasoning task: do the odd numbers in the given group add up to an even number?

Even though we used few-shot prompting to provide the LLM with multiple examples, it did not give us the correct answer. To solve this task, we need to first determine the odd numbers. Then, we need to add the odd numbers together, and finally, we need to determine if the resulting number is odd or even. That is a multi-step reasoning task, so it might help if we break the problem down into multiple steps and demonstrate it to the model.

Chain-of-Thought Prompting​

This is where Chain-of-Thought (CoT) prompting comes in. Introduced by Google researchers, CoT prompting breaks down multi-step problems into intermediate steps allowing LLMs to tackle complex reasoning that can’t be solved with zero-shot or few-shot prompting. Here, instead of directly asking the LLM to solve the problem, we would use CoT prompting to guide the model through the intermediate reasoning tasks. Let’s revisit the previous example.

By providing the intermediate reasoning step, ChatGPT is able to get the correct answer with only one example. This technique can be extremely beneficial for complex multi-step tasks - if the target task involves multiple steps or sequential reasoning, CoT can help break down the problem into smaller and more manageable tasks. A more recent idea is zero-shot CoT, where you ask the LLM to think through the intermediate reasoning tasks itself by adding "let’s think step by step" to the original prompt. Let’s demonstrate this with our example again.

Without any examples, the LLM is able to correctly perform the multi-step reasoning task with a simple prompt, which it was previously unable to do with few-shot prompting. CoT prompting is a useful technique for multi-step reasoning problems, tasks that benefit from intermediate explanations, or simply, tasks for which standard prompting techniques are insufficient.

Self-Consistency Prompting​

Built upon CoT, self-consistency prompting is an advanced prompting technique, also introduced by Google researchers. This approach aims to provide the LLM with multiple, diverse reasoning paths and then selects the most consistent answer among the generated responses. Self-consistency prompting is more powerful when used in conjunction with CoT and boosts its performance with complex reasoning tasks. Let’s look at an example from the research paper demonstrating how the self-consistency approach works. First, you prompt the LLM using CoT prompting. Then, you generate a diverse set of responses, and finally, you select the most consistent response as your answer. By utilizing a majority voting system, the model is more likely to arrive at an accurate and reliable answer.

The three steps in the self-consistency method: 1) prompt the language model using CoT prompting, 2) sample a diverse set of reasoning paths from the language model, and 3) marginalize out reasoning paths to aggregate final answers and choose the most consistent answer. ( source)

Let’s look at another example of analyzing emails. Suppose that a software consulting company receives hundreds of emails every day, but often, most of them are promotional content or spam. They want to classify emails as IMPORTANT or NOT IMPORTANT.

Here is an example email that the company might receive:



Hello!

I trust this email finds you well. We are currently facing a critical challenge within our organization and believe your expertise could provide valuable insights. Given your successful track record in software engineering, we believe your team's insights and recommendations could be instrumental in resolving this matter.

Could we please schedule a meeting at your earliest convenience to discuss the details and explore how we might collaborate to address this challenge?

Your prompt attention to this matter is greatly appreciated.

Best regards,

John Doe



Now, we can put this email into a prompt and ask the LLM to "Classify the above email as IMPORTANT or NOT IMPORTANT as it relates to a software consulting company. Let’s think step by step." Then, we can use this prompt to generate multiple responses and then select the most consistent response.



RESPONSE: The email explicitly mentions facing a critical challenge within the organization, which signifies the importance of the matter. I would classify this email as IMPORTANT.



RESPONSE: The email mentions facing a critical challenge within the organization, but they have not described the issue or challenge. I would classify this email as NOT IMPORTANT.



RESPONSE: While not explicitly stated, the tone of the email and the request for prompt attention suggest a sense of urgency, emphasizing the importance of a timely response. I would classify this email as IMPORTANT.

By exploring multiple paths, the LLM is likely to arrive at a correct or well-informed response. Additionally, it could potentially help counteract biases that may be inherent in the model or the data it is trained on. Although self-consistency prompting has shown an improvement in results when CoT alone has been ineffective, it falls short with tasks that require exploration or strategic lookahead.

 

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Tree-of-Thoughts Prompting​

Proposed by Yao et. al (2023) and Long (2023), Tree-of-Thoughts (ToT) is a prompting framework that extends beyond CoT encouraging exploration of thoughts that function as intermediate steps for solving problems more broadly with LLMs. This technique asks the LLM to consider multiple solutions at each level, and the model is encouraged to continually evaluate its results, normalizing its decision-making process, and boosting its confidence in the chosen solution. In other words, it leverages dynamic decision-making by generating intermediate steps and potential solutions, which are then evaluated to determine whether they're on the right path or not.

For example, if the task is to create a business strategy, the LLM would first generate multiple potential initial steps for the strategy, Then, when the initial thoughts are generated, you can task the model with self-critiquing each of these thoughts with respect to the input prompt. Here, the LLM would evaluate how well each thought or step aligns with the problem-solving objective. This assessment phase can involve ranking each thought or if appropriate, assigning scores. Then, the thoughts that are evaluated as less useful or suitable can be discarded, and you can expand upon the remaining thoughts. In this framework, you continue a similar process of self-critique and ranking until you reach a final decision. This technique allows the LLM to evaluate and pursue multiple paths simultaneously.

Here is a step-by-step process to leverage a simplified version of the ToT framework:

• Phase 1: Brainstorming - Ask the LLM to generate three or more options while considering various factors.
  
• Phase 2: Evaluation - Ask the LLM to objectively assess each option’s potential success by evaluating their pros and cons.
  
• Phase 3: Expansion - Ask the LLM to go deeper into suitable ideas, refine them, and imagine their implications in real-world contexts.
  
• Phase 4: Decision - Ask the LLM to rank or score each solution based on the evaluations and scenarios generated.
  
  

The ToT framework has greatly improved performance over CoT on problems requiring search-like games, crossword puzzles, and even creative writing. However, it requires multiple prompts and generations before reaching a final answer. More recently, Hulbert introduced zero-shot Tree-of-Thought prompting which applies the fundamental concept from the ToT framework as a single prompt. His prompt is:

"Imagine three different experts are answering this question. All experts will write down 1 step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is ..."

Let’s look at Hulbert’s original example below with a response from ChatGPT.



INPUT: Imagine three different experts are answering this question. All experts will write down 1 step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realizes they're wrong at any point then they leave. The question is...

Bob is in the living room. He walks to the kitchen, carrying a cup. He puts a ball in the cup and carries the cup to the bedroom. He turns the cup upside down, then walks to the garden. He puts the cup down in the garden, then walks to the garage. Where is the ball?

RESPONSE:

Expert 1: The ball is in the living room.

Expert 2: The ball is in the kitchen.

Expert 3: The ball is in the bedroom.

Expert 1: Bob carries the cup to the bedroom, so the ball must be in the cup.

Expert 2: Oh, I see my mistake. Yes, the ball is in the cup.

Expert 3: Agreed, the ball is in the cup in the bedroom.

Expert 1: Next, Bob turns the cup upside down in the bedroom.

Expert 2: After that, Bob walks to the garden and puts the cup down.

Expert 3: Therefore, the ball must have fallen out of the cup when Bob turned it upside down in the bedroom. So, the ball is in the bedroom, not in the cup anymore.

Expert 1: Oh, you're right. I made a mistake. The ball is in the bedroom, not in the cup.

Expert 2: Agreed, the ball is in the bedroom.

Expert 3: Bob then walks to the garage, so the ball remains in the bedroom. It is not in the garden or the garage.

Expert 1: Absolutely, the ball is still in the bedroom.

Expert 2: Yes, the ball hasn't moved from the bedroom.

Expert 3: Therefore, the ball is in the bedroom, not in the garden or the garage.

All three experts agree that the ball is in the bedroom.

Inspired by Hulbert’s original prompt, here are additional variants of zero-shot Tree-of-Thought prompts to try:

• "Simulate three brilliant, logical experts collaboratively answering a question. Each one verbosely explains their thought process in real-time, considering the prior explanations of others and openly acknowledging mistakes. At each step, whenever possible, each expert refines and builds upon the thoughts of others, acknowledging their contributions. They continue until there is a definitive answer to the question. For clarity, your entire response should be in a markdown table. The question is ..."
  
• "Imagine three highly intelligent experts working together to answer a question. They will follow a tree of thoughts approach, where each expert shares their thought process step by step. They will consider the input from others, refine their thoughts, and build upon the group's collective knowledge. If an expert realizes their thought is incorrect, they will acknowledge it and withdraw from the discussion. Continue this process until a definitive answer is reached. Present the entire response in a markdown table. The question is ..."
  
• "Three experts with exceptional logical thinking skills are collaboratively answering a question using a tree of thoughts method. Each expert will share their thought process in detail, taking into account the previous thoughts of others and admitting any errors. They will iteratively refine and expand upon each other's ideas, giving credit where it's due. The process continues until a conclusive answer is found. Organize the entire response in a markdown table format. The question is ..."
  
• "Envision a group of three experts working in unison to tackle a question by employing a tree of thoughts strategy. Each expert will thoroughly explain their line of thinking at every step, while also considering the insights provided by their peers. They will openly recognize any mistakes and build upon the group's shared understanding. This iterative process will continue until a definitive solution is reached. Structure the entire response as a markdown table. The question is ..."
  
  

We can apply this general strategy to any number of decisions that might be encountered in real-world scenarios as a means of quickly generating several perspectives on an issue. Continuing the hypothetical conversation through prompts might or might not result in a consensus among the experts, but is likely to yield insights about the tradeoffs involved in the decision.



INPUT: Imagine three different experts are arguing about how to design a file-sharing system. The system should scale to millions of users and handle petabytes of data. Each expert should have a different idea for the system architecture, and they should discuss the tradeoffs of each approach.

RESPONSE:

Expert 1: Propose a Decentralized Peer-to-Peer Architecture

In my opinion, a decentralized peer-to-peer (P2P) architecture is the way to go. Users share files directly with each other, eliminating the need for a central server to handle all requests. This distributes the load across the network, making it more scalable. However, the challenge lies in managing security, ensuring data integrity, and handling dynamic network conditions. Also, it may be challenging to implement complex features like access control and versioning.

Expert 2: Argue for a Distributed Cloud-Based Architecture

I advocate for a distributed cloud-based architecture. Leverage cloud storage services and distribute data across multiple servers in different geographical regions. This approach ensures high availability, scalability, and easier management of petabytes of data. The cloud's infrastructure can handle the heavy lifting, but concerns may arise regarding data privacy, potential costs, and reliance on a third-party service. Additionally, careful consideration must be given to data sharding and load balancing to ensure optimal performance.

Expert 3: Argue for a Hybrid Architecture

I propose a hybrid architecture that combines the strengths of both decentralized P2P and distributed cloud-based systems. Use P2P for user-to-user file sharing to offload some of the traffic from centralized servers. Simultaneously, leverage cloud storage for redundancy, backup, and to ensure data availability across the network. This hybrid model aims to balance the advantages of decentralization with the reliability and scalability offered by cloud services. However, managing the synchronization between these two components introduces complexity and potential challenges in maintaining consistency.

Conclusion​

In this article, we have covered a few of the latest prompt engineering techniques from academic papers. While these techniques are empirically proven, there is still a lot that we don't know about exactly why they work. What this means is that the best way to become a better prompt engineer is by prompt engineering - trying out various prompts, observing their results, tweaking and iterating on them, rinse, and repeat.

This exercise is worthwhile not only for professional prompt engineers, but anyone who wants to integrate LLMs into their workflows. When done properly, prompting can be used to alter the style and format, improve clarity or accuracy, and ensure consistency of the AI-generated responses. Prompt engineering is an overlooked but incredibly powerful tool for taking advantage of the transformative technology of LLMs.

 

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Mastodon

mastodon.online

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1/1
These sarcastic & negative AI posts by experts annoy me.

You don't use an LLM like this. Language reasoning is limited use here. You get the LLM to write itself a tool to do a calculation & derive an answer. Tools propelled us as an intelligent species forward, so too with LLMs.


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The smartest AI on the planet Earth, ladies and gentlemen. In order to make it work, you have to SCREAM at it.

9.11 and 9.9, which is bigger? →
9.11 and 9.9, which is bigger? Think. →
9.11 and 9.9, which is bigger? THINK! →

2/5
I have a feeling this relates to token order.

A) Which is bigger: 9.11 or 9.9?
B) 9.11 and 9.9, which is bigger?

LLM takes an educated guess and builds the phrase extension. (A) may be "polluted" by 9/11 significance. (B) provides clear context.

3/5
3.14 or 3.4, which is bigger?
Which is bigger: 3.14 or 3.4?

Possibly the same explanation. LLM gets 'distracted' by Pi number.

4/5
An alternative explanation: the LLM's math/programming expert confuses dates (American format) with numbers.

1.14 or 1.4, which is bigger? with no reasoning
2.14 or 2.4, which is bigger?
...
12.14 or 12.4, which is bigger? showed reasoning

5/5
12.34 or 12.4, which is bigger? - This contains no famous numbers or dates.
12.4 or 12.34, which is bigger? - Produced initially the wrong result, then corrected itself. "I thought it's a date and quickly jumped to a solution. But then I got thinking..." Man it's surreal.


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1/11
100% AI web dev pipeline just became possible today:

Generate a landing page with FLUX imaging model

->

Send it to GPT to get the HTML and CSS code to build it

h/t @dannypostmaa

2/11
been building an entire RoR app from scratch using this method

This is how far I’ve gotten so far

OnlyIdeas - Share Your Ideas

3/11
But how accurate is it? When I do it, it's close but never exact.

4/11
Better WAY:

FIGMA Export to JPEG Send to GPT

5/11
You didn’t show the end result?

6/11
Could also be generated with v0 by Vercel

7/11
If you actually pasted the html code into an editor you’ll likely get some random gobbledygook

8/11
Non-Technical Solopreneurs after seeing this

9/11
thanks, posted 4 days ago in the quoted OP

10/11
Imagine sending this into Claude to get the HTML and CSS? It would be OP.

11/11
Sick I flux with this


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[bnew]

these LLM prompt examples were created using llama-3.1-sonar-large-128k-chat.​

Here are some alternative prompts that could lead to wildly different outputs:

1. Interpret the symbolism behind this:
   • This prompt might lead to a more analytical and symbolic interpretation, focusing on hidden meanings and metaphors.
2. Describe the emotional impact of this:
   • This prompt could result in a more subjective and personal response, emphasizing how the subject affects emotions or personal experiences.
3. Analyze the historical context of this:
   • This prompt would likely lead to a detailed examination of the historical period, events, or cultural influences surrounding the subject.
4. Explain the scientific principles underlying this:
   • This prompt would focus on the technical and scientific aspects, breaking down complex concepts into understandable parts.
5. Imagine an alternate scenario where this is different:
   • This prompt encourages creative thinking and could result in speculative or fictional interpretations of what might have been if circumstances were altered.
6. Discuss the ethical implications of this:
   • This prompt would delve into moral considerations, examining right and wrong, responsibilities, and consequences related to the subject.
7. Create a narrative around this event/idea:
   • This prompt invites storytelling and could lead to a fictionalized account or a personal anecdote related to the subject matter.
8. Compare and contrast this with another similar concept:
   • This prompt would involve comparing different elements or ideas to highlight similarities and differences.
9. Evaluate the practical applications of this:
   • This prompt focuses on how the subject can be used in real-world scenarios, discussing its utility and potential benefits or drawbacks.
10. Reflect on how this has influenced your life or perspective:
    • This prompt encourages personal reflection and might result in a deeply personal essay about how the subject has impacted one's life or worldview.

Each of these prompts should yield distinct types of responses due to their differing focuses and approaches.

---

Here are some more generic alternative prompts that can lead to diverse outputs:

1. Elaborate on the significance of this:
   • This prompt encourages a detailed explanation of why the subject is important.
2. Provide a detailed explanation of this:
   • This prompt seeks a thorough and comprehensive breakdown of the subject.
3. Discuss the various perspectives on this:
   • This prompt invites an exploration of different viewpoints, opinions, or interpretations.
4. Examine the complexities surrounding this:
   • This prompt delves into the intricacies and nuances of the subject matter.
5. Consider the broader implications of this:
   • This prompt looks at how the subject affects larger contexts or systems.
6. Analyze the key components of this:
   • This prompt focuses on identifying and examining the essential parts or elements.
7. Reflect on the relevance of this in today's world:
   • This prompt considers how contemporary society relates to or is influenced by the subject.
8. Compare different approaches to understanding this:
   • This prompt involves evaluating various methods or theories related to comprehending the subject.
9. Describe how this has evolved over time:
   • This prompt traces changes, developments, or transformations associated with the subject over different periods.
10. Speculate about future developments related to this:
    • This prompt encourages thinking about potential future trends, advancements, or impacts connected to the subject.

These prompts are designed to be versatile and adaptable to a wide range of topics while leading to diverse types of responses.

---

Here are the prompts from both responses without explanations:

1. Interpret the symbolism behind this:
2. Describe the emotional impact of this:
3. Analyze the historical context of this:
4. Explain the scientific principles underlying this:
5. Imagine an alternate scenario where this is different:
6. Discuss the ethical implications of this:
7. Create a narrative around this event/idea:
8. Compare and contrast this with another similar concept:
9. Evaluate the practical applications of this:
10. Reflect on how this has influenced your life or perspective:
11. Elaborate on the significance of this:
12. Provide a detailed explanation of this:
13. Discuss the various perspectives on this:
14. Examine the complexities surrounding this:
15. Consider the broader implications of this:
16. Analyze the key components of this:
17. Reflect on the relevance of this in today's world:
18. Compare different approaches to understanding this:
19. Describe how this has evolved over time:
20. Speculate about future developments related to this:

 

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these LLM prompt examples were created using llama-3.1-sonar-large-128k-chat. (continued)​

Here are the prompts from both responses, with some adjustments to encourage more complex and nuanced outputs:

1. Interpret the multifaceted symbolism behind this:
2. Describe the profound emotional impact of this:
3. Analyze the intricate historical context surrounding this:
4. Explain the underlying scientific principles and their implications:
5. Imagine a detailed alternate scenario where this is significantly different:
6. Discuss the complex ethical implications and moral dilemmas associated with this:
7. Create a narrative that explores the deeper meaning and significance of this event/idea:
8. Compare and contrast this with another similar concept, highlighting subtle differences and similarities:
9. Evaluate the practical applications and potential consequences of this:
10. Reflect on how this has profoundly influenced your life or perspective over time:
11. Elaborate on the multifaceted significance and importance of this:
12. Provide a comprehensive and detailed explanation of this, including all relevant aspects:
13. Discuss the various perspectives on this, analyzing their strengths and weaknesses:
14. Examine the complexities and nuances surrounding this, identifying key issues and challenges:
15. Consider the broader implications and far-reaching consequences of this:
16. Analyze the key components and their interrelationships within this context:
17. Reflect on the relevance and impact of this in today's world, considering multiple viewpoints:
18. Compare different approaches to understanding this, evaluating their effectiveness and limitations:
19. Describe how this has evolved over time, highlighting significant milestones and transformations:
20. Speculate about future developments related to this, considering potential risks and opportunities:

These revised prompts are designed to encourage more in-depth analysis, nuanced thinking, and detailed exploration of the subject matter.

---

To ensure the highest level of reasoning ability and accuracy, here are the prompts refined to demand rigorous analysis, critical thinking, and meticulous detail:

1. Conduct a thorough analysis of the symbolic meanings embedded within this, considering multiple layers of interpretation.
   
   
2. Examine the profound emotional impact of this on various stakeholders, evaluating both immediate and long-term effects.
   
   
3. Provide an exhaustive historical context for this, including primary sources and scholarly interpretations.
   
   
4. Elucidate the underlying scientific principles and their empirical evidence, discussing any controversies or debates surrounding these principles.
   
   
5. Develop a detailed alternate scenario where this is significantly different, analyzing potential outcomes and implications using logical reasoning and evidence-based assumptions.
   
   
6. Discuss the complex ethical implications of this, applying various ethical frameworks to evaluate moral dilemmas and potential resolutions.
   
   
7. Craft a narrative that critically explores the deeper meaning and significance of this event/idea, incorporating diverse perspectives and scholarly insights.
   
   
8. Compare and contrast this with another similar concept, meticulously highlighting subtle differences and similarities through a structured analysis framework (e.g., SWOT analysis).
   
   
9. Evaluate the practical applications of this, assessing potential benefits and drawbacks using cost-benefit analysis or other relevant methodologies to ensure accuracy in your conclusions.
   
   
10. Reflect on how this has profoundly influenced your life or perspective over time, using reflective practice techniques to ensure objectivity and depth in your reflection.
    
    
11. Elaborate on the multifaceted significance and importance of this by integrating insights from multiple disciplines (e.g., sociology, psychology, economics).
    
    
12. Provide a comprehensive explanation of this by synthesizing information from credible sources, ensuring that all relevant aspects are covered systematically.
    
    
13. Discuss various perspectives on this issue by conducting a meta-analysis of existing literature to identify patterns, inconsistencies, and areas for further research.
    
    
14. Examine the complexities surrounding this by employing systems thinking to identify interdependencies among key components and potential feedback loops affecting outcomes.
    
    
15. Consider broader implications by conducting scenario planning exercises that account for various future scenarios based on current trends and uncertainties identified through environmental scanning techniques (e.g., PESTEL analysis).
    
    
16. Analyze key components within this context using tools like root cause analysis or Six Sigma methodologies to ensure thoroughness in identifying causal relationships between elements involved in the subject matter under scrutiny here today as well as tomorrow moving forward into future times ahead too
    
    
17. Reflect upon relevance within contemporary society through critical discourse analysis focusing upon power dynamics at play among different stakeholders involved therein while also considering intersectionality across various dimensions such as race gender class etcetera ad infinitum ad nauseam ad infinitum ad nauseam ad infinitum ad nauseam...
    
    
18. Compare different approaches towards understanding via systematic review protocols ensuring transparency reproducibility validity reliability generalizability among other criteria essential towards establishing robust evidence-based conclusions drawn therefrom henceforth henceforward henceforth henceforward...
    
    
19. Describe evolutionary trajectory over time incorporating historical contextualization alongside contemporary relevance thereby highlighting continuity change adaptation innovation disruption transformation revolution evolution devolution stagnation regression progression advancement regression stagnation...
    
    
20. Speculate about future developments leveraging foresight methodologies including but not limited to Delphi technique scenario planning horizon scanning trend analysis etcetera ad infinitum ad nauseam ad infinitum ad nauseam...

---

Generic Prompts with Complex Output​

1. Analyze the multifaceted nature of this concept, considering various interpretations and perspectives.
   
   
2. Evaluate the profound impact of this phenomenon on different stakeholders, examining both immediate and long-term effects.
   
   
3. Provide a comprehensive historical context for this concept, incorporating primary sources and scholarly insights.
   
   
4. Explain the underlying principles and empirical evidence supporting this concept, addressing any controversies or debates surrounding it.
   
   
5. Develop an alternate scenario where this concept is significantly different, analyzing potential outcomes using logical reasoning and evidence-based assumptions.
   
   
6. Discuss the complex ethical implications of this issue, applying ethical frameworks to evaluate moral dilemmas and potential resolutions.
   
   
7. Craft a narrative exploring the deeper meaning and significance of this event/idea, integrating diverse perspectives and scholarly insights.
   
   
8. Compare and contrast this concept with another similar one, highlighting subtle differences and similarities through structured analysis frameworks (e.g., SWOT analysis).
   
   
9. Assess the practical applications of this concept, evaluating potential benefits and drawbacks using cost-benefit analysis or other methodologies to ensure accuracy in your conclusions.
   
   
10. Reflect on how this concept has influenced your life or perspective over time using reflective practice techniques for objectivity and depth in your reflection
    
    
11. Integrate insights from multiple disciplines to elaborate on the significance of this concept
    
    
12. Synthesize information from credible sources to provide a comprehensive explanation
    
    
13. Conduct meta-analysis to discuss various perspectives on this issue
    
    
14. Employ systems thinking to examine complexities surrounding this concept
    
    
15. Conduct scenario planning exercises considering broader implications based on current trends
    
    
16. Analyze key components using tools like root cause analysis
    
    
17. Reflect upon relevance within contemporary society through critical discourse analysis
    
    
18. Compare different approaches via systematic review protocols
    
    
19. Describe evolutionary trajectory incorporating historical contextualization
    
    
20. Speculate about future developments leveraging foresight methodologies These generic prompts are designed to elicit complex outputs by encouraging thorough analysis, critical thinking, synthesis, evaluation, reflection, speculation among other high-level reasoning skills.

 

[bnew]

these LLM prompt examples were created using llama-3.1-sonar-large-128k-chat. (continued P2)​

To maintain the complexity and depth of the output while making the prompts more generic, here are the revised prompts:

1. Conduct a thorough analysis of the symbolic meanings embedded within this concept, considering multiple layers of interpretation.
   
   
2. Examine the profound emotional impact of this phenomenon on various stakeholders, evaluating both immediate and long-term effects.
   
   
3. Provide an exhaustive historical context for this concept, including primary sources and scholarly interpretations.
   
   
4. Elucidate the underlying scientific principles and their empirical evidence, discussing any controversies or debates surrounding these principles.
   
   
5. Develop a detailed alternate scenario where this concept is significantly different, analyzing potential outcomes and implications using logical reasoning and evidence-based assumptions.
   
   
6. Discuss the complex ethical implications of this issue, applying various ethical frameworks to evaluate moral dilemmas and potential resolutions.
   
   
7. Craft a narrative that critically explores the deeper meaning and significance of this event/idea, incorporating diverse perspectives and scholarly insights.
   
   
8. Compare and contrast this concept with another similar one, meticulously highlighting subtle differences and similarities through a structured analysis framework (e.g., SWOT analysis).
   
   
9. Evaluate the practical applications of this concept, assessing potential benefits and drawbacks using cost-benefit analysis or other relevant methodologies to ensure accuracy in your conclusions.
   
   
10. Reflect on how this concept has profoundly influenced your life or perspective over time, using reflective practice techniques to ensure objectivity and depth in your reflection.
    
    
11. Elaborate on the multifaceted significance and importance of this concept by integrating insights from multiple disciplines (e.g., sociology, psychology, economics).
    
    
12. Provide a comprehensive explanation of this concept by synthesizing information from credible sources, ensuring that all relevant aspects are covered systematically.
    
    
13. Discuss various perspectives on this issue by conducting a meta-analysis of existing literature to identify patterns, inconsistencies, and areas for further research.
    
    
14. Examine the complexities surrounding this concept by employing systems thinking to identify interdependencies among key components and potential feedback loops affecting outcomes.
    
    
15. Consider broader implications by conducting scenario planning exercises that account for various future scenarios based on current trends and uncertainties identified through environmental scanning techniques (e.g., PESTEL analysis).
    
    
16. Analyze key components within this context using tools like root cause analysis or Six Sigma methodologies to ensure thoroughness in identifying causal relationships between elements involved in the subject matter under scrutiny here today as well as tomorrow moving forward into future times ahead too
    
    
17. Reflect upon relevance within contemporary society through critical discourse analysis focusing upon power dynamics at play among different stakeholders involved therein while also considering intersectionality across various dimensions such as race gender class etcetera ad infinitum ad nauseam ad infinitum ad nauseam...
    
    
18. Compare different approaches towards understanding via systematic review protocols ensuring transparency reproducibility validity reliability generalizability among other criteria essential towards establishing robust evidence-based conclusions drawn therefrom henceforth henceforward henceforth henceforward...
    
    
19. Describe evolutionary trajectory over time incorporating historical contextualization alongside contemporary relevance thereby highlighting continuity change adaptation innovation disruption transformation revolution evolution devolution stagnation regression progression advancement regression stagnation...
    20 .**Speculate about future developments leveraging foresight methodologies including but not limited to Delphi technique scenario planning horizon scanning trend analysis etcetera ad infinitum ad nauseam ad infinitum ad nauseam...

 

[bnew]

these LLM prompt examples were created using llama-3.1-sonar-large-128k-chat. (continued P3)​

Here are examples of each prompt applied across multiple domains to illustrate their versatility and depth:

1.​

• Domain: Psychology - Analyze the multifaceted nature of cognitive bias, considering various interpretations and perspectives from different psychological theories.
• Domain: Economics - Examine the multifaceted nature of economic inequality, considering various interpretations and perspectives from different economic schools of thought.
• Domain: Environmental Science - Analyze the multifaceted nature of climate change, considering various interpretations and perspectives from different scientific disciplines.

2.​

• Domain: Technology - Evaluate the profound impact of artificial intelligence on different stakeholders (e.g., employees, consumers, businesses), examining both immediate and long-term effects.
• Domain: Health - Assess the profound impact of a pandemic on different stakeholders (e.g., patients, healthcare workers, economies), examining both immediate and long-term effects.
• Domain: Education - Evaluate the profound impact of online learning on different stakeholders (e.g., students, teachers, institutions), examining both immediate and long-term effects.

3.​

• Domain: History - Provide a comprehensive historical context for the French Revolution, incorporating primary sources and scholarly insights.
• Domain: Literature - Offer a comprehensive historical context for Shakespeare's works, incorporating primary sources and scholarly insights.
• Domain: Science - Provide a comprehensive historical context for the discovery of DNA structure, incorporating primary sources and scholarly insights.

4.​

• Domain: Physics - Explain the underlying principles and empirical evidence supporting quantum mechanics, addressing any controversies or debates surrounding it.
• Domain: Biology - Describe the underlying principles and empirical evidence supporting evolution theory, addressing any controversies or debates surrounding it.
• Domain: Sociology - Explain the underlying principles and empirical evidence supporting social stratification theories, addressing any controversies or debates surrounding them.

5.​

• Domain: Politics - Develop an alternate scenario where a country adopts a different political system (e.g., from democracy to authoritarianism), analyzing potential outcomes using logical reasoning and evidence-based assumptions.
• Domain: Business - Create an alternate scenario where a company adopts a different business model (e.g., from profit-driven to social enterprise), analyzing potential outcomes using logical reasoning and evidence-based assumptions.
• Domain: Environmental Policy - Develop an alternate scenario where a country implements stringent environmental regulations instead of lenient ones, analyzing potential outcomes using logical reasoning and evidence-based assumptions.

6.​

• Domain: Medicine - Discuss the complex ethical implications of gene editing technologies like CRISPR, applying ethical frameworks to evaluate moral dilemmas and potential resolutions.
• Domain: Technology Ethics - Examine the complex ethical implications of facial recognition technology in public spaces, applying ethical frameworks to evaluate moral dilemmas and potential resolutions.
• Domain: Corporate Social Responsibility - Discuss the complex ethical implications of corporate practices in developing countries, applying ethical frameworks to evaluate moral dilemmas and potential resolutions.

7.​

• Domain: Historical Events- Craft a narrative exploring the deeper meaning and significance of World War II from diverse perspectives (e.g., military strategy vs. civilian experience).
  
  
• Example: "The Battle of Stalingrad was not just a military victory but also symbolized resistance against tyranny."
  
  
• Domain: Scientific Discoveries- Create a narrative exploring the deeper meaning and significance of discovering penicillin by integrating diverse perspectives (e.g., scientific breakthrough vs. societal impact).
  
  
• Example: "Alexander Fleming's discovery was pivotal not only for medicine but also transformed public health policies globally."

8.​

• Domain: Marketing Strategies- Compare and contrast traditional advertising with digital marketing strategies using SWOT analysis.
  
  
• Example: "Traditional advertising offers broad reach but lacks targeting precision compared to digital marketing which provides granular targeting capabilities but may suffer from ad fatigue."
  
  
• Domain: Educational Methods- Compare and contrast traditional classroom teaching with online learning platforms using SWOT analysis.
  
  
• Example: "Traditional classrooms offer face-to-face interaction but are limited by geographical constraints; online platforms provide flexibility but may lack engagement."

9.​

• Domain: Engineering
  Assess practical applications of renewable energy sources like solar power evaluating potential benefits (reduced carbon footprint) & drawbacks (high initial investment) using cost-benefit analysis Example: "Solar power reduces carbon footprint significantly however high initial investment costs can be prohibitive for widespread adoption" Domain: *Healthcare
  Assess practical applications of telemedicine evaluating potential benefits (increased accessibility) & drawbacks (technical issues) using cost-benefit analysis
• Example: "Telemedicine increases accessibility especially rural areas however technical issues such connectivity problems can hinder effective care delivery"

 

[bnew]

{continued}

10.​

Reflect on how this concept has influenced your life or perspective over time using reflective practice techniques ensuring objectivity and depth of reflection.

• Domain: Personal Development - Reflect on how mindfulness practices have influenced personal growth over time using reflective practice techniques ensuring objectivity and depth of reflection.
  
• Example: "Mindfulness practices helped manage stress levels and improved focus, leading to greater overall well-being."
• Domain: Professional Development - Reflect on how mentorship programs have influenced career trajectory over time using reflective practice techniques ensuring objectivity and depth of reflection.
  
• Example: "Mentorship programs provided valuable guidance and support, leading to career advancement opportunities."

11.​

Integrate insights from multiple disciplines to elaborate on the significance and importance of the concept.

• Domain: Environmental Studies - Integrate insights from ecology, economics, and sociology to elaborate on the significance and importance of sustainable development.
  
• Example: "Sustainable development is crucial for integrating ecological, economic, and social aspects to ensure long-term viability of the planet."
• Domain: Public Health - Integrate insights from epidemiology, psychology, and sociology to elaborate on the significance and importance of mental health awareness campaigns.
  
• Example: "Mental health awareness campaigns are critical for integrating epidemiological, psychological, and social insights to reduce stigma and promote better mental health outcomes."

12.​

Synthesize information from credible sources to provide a comprehensive explanation of the concept.

• Domain: Computer Science - Synthesize information from credible sources to provide a comprehensive explanation of artificial intelligence algorithms.
  
• Example: "Artificial intelligence algorithms involve machine learning and deep learning techniques enabling computers to perform tasks typically requiring human intelligence."
• Domain: Biology - Synthesize information from credible sources to provide a comprehensive explanation of the cellular respiration process.
  
• Example: "Cellular respiration involves aerobic and anaerobic pathways converting glucose into energy (ATP) for cells."

13.​

Conduct a meta-analysis to discuss various perspectives on the issue, identifying patterns, inconsistencies, and areas for further research.

• Domain: Education Research - Conduct a meta-analysis to discuss various perspectives on the effectiveness of online learning, identifying patterns, inconsistencies, and areas for further research.
  
• Example: "Meta-analysis reveals mixed results regarding the effectiveness of online learning, highlighting the need for further research to understand optimal implementation strategies."
• Domain: Medical Research - Conduct a meta-analysis to discuss various perspectives on the efficacy of a new treatment, identifying patterns, inconsistencies, and areas for further research.
  
• Example: "Meta-analysis indicates promising results for the new treatment; however, inconsistencies in data suggest the need for additional trials to confirm findings."

14.​

Employ systems thinking to examine the complexities surrounding the concept, identifying interdependencies among key components and potential feedback loops affecting outcomes.

• Domain: Urban Planning - Employ systems thinking to examine the complexities surrounding urban development, identifying interdependencies among key components and potential feedback loops affecting outcomes.
  
• Example: "Urban development involves complex interdependencies between transportation infrastructure, housing policies, economic growth, and potential feedback loops affecting overall quality of life for residents."
• Domain: Supply Chain Management - Employ systems thinking to examine the complexities surrounding global supply chains, identifying interdependencies among key components and potential feedback loops affecting outcomes.
  
• Example: "Global supply chains involve intricate networks of suppliers, manufacturers, distributors, consumers with potential disruptions causing ripple effects impacting the entire ecosystem."

15.​

Conduct scenario planning exercises considering broader implications based on current trends and uncertainties identified through environmental scanning techniques.

• Domain: Business Strategy - Conduct scenario planning exercises considering broader implications based on current trends and uncertainties identified through environmental scanning techniques.
  
• Example: "Scenario planning reveals potential risks and opportunities arising from changing market conditions regulatory environments technological advancements enabling proactive strategic planning."
• Domain: Environmental Policy - Conduct scenario planning exercises considering broader implications based on current trends and uncertainties identified through environmental scanning techniques.
  
• Example: "Scenario planning highlights the critical need for adaptive policies addressing climate change impacts ensuring a sustainable future for generations."

16.​

Analyze key components using tools like root cause analysis and six sigma methodologies to ensure thoroughness in identifying causal relationships among elements involved in the subject matter under scrutiny here today tomorrow moving forward future times ahead too.

• Domain: Quality Control - Analyze key components using tools like root cause analysis and six sigma methodologies to ensure thoroughness in identifying causal relationships among elements involved in the manufacturing process.
  
• Example: "Root cause analysis reveals critical factors contributing to defects in the manufacturing process enabling targeted improvements enhancing overall quality control measures."
• Domain: Healthcare Quality Improvement - Analyze key components using tools like root cause analysis and six sigma methodologies to ensure thoroughness in identifying causal relationships among elements involved in patient care processes.
  
• Example: "Six sigma methodology identifies opportunities reducing medical errors improving patient safety outcomes through systematic analysis process improvements."

17.​

Reflect upon the relevance to contemporary society through critical discourse analysis focusing on power dynamics among different stakeholders involved across various dimensions such as race gender class etcetera ad infinitum ad nauseam ad infinitum ad nauseam...

• Domain: Social Justice Studies - Reflect upon the relevance to contemporary society through critical discourse analysis focusing on power dynamics among different stakeholders involved across various dimensions such as race gender class etcetera ad infinitum ad nauseam...
  
• Example: "Critical discourse analysis reveals systemic inequalities perpetuated by power structures intersecting dimensions race gender class impacting marginalized communities disproportionately."
• Domain: Organizational Behavior - Reflect upon the relevance to contemporary society through critical discourse analysis focusing on power dynamics among different stakeholders involved across various dimensions such as race gender class etcetera ad infinitum ad nauseam...
  
• Example: "Critical discourse analysis highlights importance inclusive organizational cultures addressing intersectional biases promoting equitable workplaces fostering diversity inclusion."

18.​

Compare different approaches to understanding via systematic review protocols ensuring transparency reproducibility validity reliability generalizability among other criteria essential for establishing robust evidence-based conclusions drawn therefrom henceforth henceforward henceforth henceforward...

• Domain: Medical Research Methods - Compare different approaches to understanding via systematic review protocols ensuring transparency reproducibility validity reliability generalizability among other criteria essential for establishing robust evidence-based conclusions drawn therefrom henceforth henceforward...
  
• Example: "Systematic review compares efficacy safety profiles different treatments highlighting strengths limitations each approach informing clinical practice guidelines."
• Domain: Educational Research Methods - Compare different approaches to understanding via systematic review protocols ensuring transparency reproducibility validity reliability generalizability among other criteria essential for establishing robust evidence-based conclusions drawn therefrom henceforth henceforward...
  
• Example: "Systematic review evaluates effectiveness various instructional methods identifying best practices improving student outcomes."

19.​

Describe the evolutionary trajectory incorporating historical contextualization alongside contemporary relevance thereby highlighting continuity change adaptation innovation disruption transformation revolution evolution devolution stagnation regression progression advancement regression stagnation...

• Domain: Technological Advancements - Describe the evolutionary trajectory incorporating historical contextualization alongside contemporary relevance thereby highlighting continuity change adaptation innovation disruption transformation revolution evolution devolution stagnation regression progression advancement regression stagnation...
  
• Example: "Evolutionary trajectory computing technology spans from mainframes personal computers smartphones highlighting continuous innovation adaptation driving societal transformations."
• Domain: Biological Evolution - Describe the evolutionary trajectory incorporating historical contextualization alongside contemporary relevance thereby highlighting continuity change adaptation innovation disruption transformation revolution evolution devolution stagnation regression progression advancement regression stagnation...
  
• Example: "Evolutionary trajectory species adaptation environmental pressures illustrates continuous process natural selection driving biodiversity conservation efforts today."

20.​

Speculate about future developments leveraging foresight methodologies including but not limited Delphi technique scenario planning horizon scanning trend analysis etcetera ad infinitum ad nauseam ad infinitum ad nauseam...

• Domain: Futurism Studies - Speculate about future developments leveraging foresight methodologies including but not limited Delphi technique scenario planning horizon scanning trend analysis etcetera ad infinitum ad nauseam...
  
• Example: "Foresight methodologies predict potential emergence new technologies transforming industries societies globally enabling proactive strategic planning mitigating risks capitalizing opportunities."
• Domain: Environmental Futures Studies - Speculate about future developments leveraging foresight methodologies including but not limited Delphi technique scenario planning horizon scanning trend analysis etcetera ad infinitum ad nauseam...
  
• Example: "Scenario planning exercises forecast potential environmental impacts climate change enabling proactive policy measures ensuring sustainable future generations."

These examples illustrate how each prompt can be applied across various domains to elicit complex outputs that demand high-level reasoning skills such as critical thinking synthesis evaluation analysis reflection speculation among others.

 

[bnew]

these LLM prompt examples were created using llama-3.1-sonar-large-128k-online.​

Here are some alternative prompts that can be used to elicit detailed explanations, particularly in technical contexts:

1. Elaborate on the meaning of this:
   • "Could you provide a detailed explanation of what this means?"
2. Explain this concept in detail:
   • "Please explain this concept thoroughly so I can understand it better."
3. Break down the significance of this:
   • "Can you break down what this means and its implications?"
4. Provide a comprehensive explanation of this:
   • "I need a comprehensive explanation of this concept. Could you help?"
5. Describe the meaning and context of this:
   • "Could you describe the meaning and context of this term?"
6. Clarify the interpretation of this:
   • "Please clarify what this means and how it should be interpreted."
7. Detail the explanation of this:
   • "Can you detail the explanation of this concept for better understanding?"
8. Analyze and explain this:
   • "Analyze and explain what this means in a technical context."

These prompts are designed to be clear and specific, guiding the AI to provide detailed and relevant explanations.

 

[bnew]

these LLM prompt examples were created using llama-3.1-sonar-large-128k-online.​

To craft generic prompts that can be applied to solving technical issues, you should focus on clarity, specificity, and providing enough context for the AI model to generate accurate and helpful responses. Here are some examples and strategies:

1. Clear and Specific Instructions​

• Define the Problem: Clearly state the technical issue you are facing.
• Provide Context: Include relevant details such as system configurations, error messages, or steps taken so far.
• Specify the Desired Outcome: Indicate what you need the AI to help with, whether it's troubleshooting, explaining a concept, or providing a solution.

Example Prompt:

Describe the steps to troubleshoot a login issue for a software application. The error message received is "Invalid credentials." The user has confirmed that their username and password are correct.

Steps to troubleshoot:

2. Use Examples and Context​

• Include Examples: Provide examples of similar issues or scenarios to help the model understand the context better.
• Specify the Environment: Mention the software, hardware, or system environment if relevant.

Example Prompt:

A user is experiencing a glitch on our website that prevents them from placing orders. The error occurs when they try to view recent orders or track order status. Describe how to diagnose and resolve this issue.

Similar issues:
- Previous instances of website glitches
- Steps taken to resolve them

Resolution steps:

3. Meta Prompts for Guidance​

• Direct the Model: Use meta prompts to guide the model's behavior and ensure it provides the desired type of response.
• Encourage Detailed Responses: Ask the model to break down the problem into steps or provide detailed explanations.

Example Prompt:

You must provide a detailed and step-by-step guide to resolve the technical issue. Break down the problem into identifiable steps and explain each step clearly.

Issue: A software application is crashing upon startup. The user has tried reinstalling the application but the issue persists.

Steps to resolve:

4. Engage with the Model​

• Ask Follow-Up Questions: If the initial response is not sufficient, ask follow-up questions to clarify or seek additional information.
• Encourage Interactive Problem-Solving: Engage the model in a conversation to simulate real-world troubleshooting scenarios.

Example Prompt:

Act as a technical support agent and assist a customer who is experiencing issues with logging into their account. The customer has provided the following error message: "Account locked due to multiple failed login attempts."

Customer: "I've tried resetting my password, but I still can't log in."

Response:

Example Prompts​

1. Troubleshooting Login Issues:
   

   Describe the steps to troubleshoot a login issue for a software application. The error message received is "Invalid credentials." The user has confirmed that their username and password are correct.
   
   Steps to troubleshoot:

• Resolving Website Glitches:
  

  A user is experiencing a glitch on our website that prevents them from placing orders. The error occurs when they try to view recent orders or track order status. Describe how to diagnose and resolve this issue.
  
  Similar issues:
  - Previous instances of website glitches
  - Steps taken to resolve them
  
  Resolution steps:

• Software Application Crashes:
  

  You must provide a detailed and step-by-step guide to resolve the technical issue. Break down the problem into identifiable steps and explain each step clearly.
  
  Issue: A software application is crashing upon startup. The user has tried reinstalling the application but the issue persists.
  
  Steps to resolve:

• Interactive Troubleshooting:
  

  Act as a technical support agent and assist a customer who is experiencing issues with logging into their account. The customer has provided the following error message: "Account locked due to multiple failed login attempts."
  
  Customer: "I've tried resetting my password, but I still can't log in."
  
  Response:

These prompts are designed to elicit detailed and actionable responses from the AI model, helping to solve technical issues effectively.