The Unseen Art of Prompt Engineering: Unleashing AI’s Full Potential 202

Foundations of Prompt Engineering: A Recap and Progression

Great to see you again, if this is your first time here and have not seen the introduction Unleashing AI’s Full Potential 101 please go ahead

Recap of Foundational Techniques

Last time we spoke we discussed thoroughly (which is my favorite adverb when using Chat GPT by the way) about Zero-shot, random few-shots, Chain of Thought and Tree of Thought. We really saw the most efficient and user friendly ways to use any AI

The Journey from Beginner to Advanced User

Because that is the most important part that, by the end of this article, you will understand: All AIs are different but at the same time are quite similar. They are, after all, built by us humans.

 

Since now you are not this beginner painter who barely can paint a flower, I will assume you have at least 50 hours using Chat GPT or any other AI you are trying to master

 

Here at GPTPotenza we will always try to give you precise tips on ChatGPT because we believe that it’s the Michael Jordan of AIs and we can talk for hours about that if you want.

 

I wanted to give you a special gift in this article and you will see it at the end of this article.

 

Let’s get into it shall we?

 

kNN Few-Shot Learning in Prompt Engineering: A Detailed Exploration

 

kNN (k-nearest neighbors) Few-Shot Learning is a technique where the AI utilizes the most relevant examples from its training to inform its responses. It’s akin to tapping into a collective wisdom, where the AI searches its vast knowledge base to find the closest matches to the prompt, providing contextually rich and precise answers.

Understanding kNN Few-Shot Learning

When leveraging kNN Few-Shot Learning, a common question arises: “How do we know what’s in the AI’s database, and how can we trust its relevance?” This is crucial because the effectiveness of kNN Few-Shot Learning hinges on the AI referencing the most appropriate and accurate examples from its training.

 

AI’s Training Data and Its Implications for Prompt Engineering

 

AI models like GPT-4 are trained on vast and diverse datasets that include a wide range of internet text. These datasets typically encompass books, articles, websites, and other publicly available written materials, covering countless topics.

However, the exact specifics of these datasets are not always publicly disclosed due to proprietary reasons. The training involves billions of words and diverse types of content to ensure a broad understanding.

 

In other words, we do not effectively know the data sets that Chat GPT was trained on and trying to get somewhere near to the database, not even after 50 attempts from my part made a scratch to the surface.

 

Open AI is really conservative about this subject. I would only like to say that it’s nice to see that there is something from the right that they actually do stand for.

 

Implications for kNN Few-Shot Learning:

 

  • Broad Coverage: Given the extensive and diverse nature of the training data, the AI has a broad understanding of many topics, from common knowledge to specialized fields.

  • Limitations: The AI’s knowledge is limited to the content it was trained on, typically up to a certain date. For instance, GPT-4’s training includes information available up until January 2022 (when I wrote this article), meaning it doesn’t have data on events or developments that occurred after that time.

  • Accuracy and Relevance: While the AI strives to provide accurate and relevant information, it’s not infallible. Users should cross-reference AI responses with up-to-date and authoritative sources, especially for critical applications like legal advice or medical diagnosis.

 

Implementing kNN Few-Shot Learning in Prompt Engineering

 

To effectively use kNN few-shot learning, prompts should be designed to guide the AI in searching and matching the most relevant examples in its training. This method is particularly useful in scenarios where precision and specificity are paramount.

 

 

Illustrative Examples of kNN Few-Shot Learning in Action

 

Legal Advisory:

 

  • Prompt: “Provide guidance on intellectual property rights for software development in the European Union.”

  • AI Response: The AI delves into its database, finding and referencing the most pertinent legal cases, directives, and regulations from the EU context.

  • Expanding Options: Based on this response, the user can explore specific legal precedents, delve deeper into particular regulations, or compare with other jurisdictions.

  • Medical Diagnosis Assistance:

 

Prompt: “Analyze symptoms of shortness of breath and chest pain in a middle-aged patient.”

  • AI Response: AI references similar patient cases and medical literature, suggesting potential diagnoses like heart conditions or respiratory issues.

  • Expanding Options: Following this, options include detailed exploration of each diagnosis, personalized treatment suggestions, or further diagnostic tests.

 

Market Research Analysis:

 

  • Prompt: “Assess the potential of a new fitness app in the Asian market.”

  • AI Response: AI pulls from related market studies, consumer trends, and app success stories in similar demographics.

  • Expanding Options: This leads to pathways such as targeting specific user segments, analyzing competitor strategies, or identifying market gaps.

 

Alright so those are a few examples that can illustrate but as usual: Lets go deeper.

Advanced Integration Techniques in Prompt Engineering

 

Let’s take the 1st example.

Provide guidance on intellectual property rights for software development in the European Union

 

Usually, if you are a user taking a look into this subject you might be knowledgeable about this topic and want to look for a specific solution and if you are not, let Chat GPT know about it.

 

IF you are a Swiss lawyer trying to find specific information to beat your counterparts in the European Union or want to find relevant information about a specific law. Your best shot is not to see what GPT knows. Your best shot at finding value inside of this new tool is this:

 

Find the actual articles that are relevant to your case. For instance, if you really want to see how europe is doing with Free/Open Source then you must already know that they are using the European Union Public License (EUPL) https://joinup.ec.europa.eu/collection/eupl/introduction-eupl-licence in that specific subject.

Then after knowing exactly what you are dealing with, download that document and then upload it to Chat GPT. Lastly, ask Chat GPT to read about that document and be as specific as you can to create remarkable results. I have made an article about it here

Combining Techniques for Enhanced AI Interaction

You can actually upload a couple of documents to increase your chances of getting great results, do not forget that.

 

kNN Few-Shot Learning empowers ChatGPT to provide responses that are not only accurate but also richly informed by a wide spectrum of human knowledge. Understanding the expanse and limitations of its training data is key to harnessing the full potential of this AI capability, enabling users to make informed decisions and gain insightful perspectives.

 

In-Depth Scenarios Illustrating Integrated Prompt Engineering

 

Combining Zero-Shot, Few-Shot, Chain of Thought, Tree of Thought, and kNN Few-Shot Learning is akin to an orchestra where each instrument plays a pivotal role. Together, they create a symphony of AI capabilities, each enhancing the overall performance. This integrated approach allows for sophisticated, nuanced interactions with AI, making it an invaluable tool across diverse applications.

 

Strategically Combining Techniques:

 

Harmonizing Strengths: Each technique contributes uniquely. Zero-Shot learning provides broad, foundational responses. Few-Shot and kNN Few-Shot Learning refine these responses with contextual details and specific examples. Chain and Tree of Thought introduce depth, revealing the AI’s reasoning or exploring multiple solution pathways.

Situational Applications: The choice of technique depends on the task. Complex challenges might require a Chain of Thought for logical progression, enhanced with Few-Shot examples for specificity and kNN Few-Shot Learning for highly relevant, contextual information.

 

In-Depth Scenarios Illustrating Integrated Prompting:

 

Developing a Business Strategy:

 

  • Integration: Start with Zero-Shot for general insights about market trends. Introduce Few-Shot learning with case studies of similar businesses for context. Employ Tree of Thought for brainstorming multiple strategic paths, such as market penetration, diversification, or innovation.

  • Elaboration: For example, when exploring market penetration, use Chain of Thought to detail steps like competitor analysis, market research, and pricing strategies. Enhance this with kNN Few-Shot Learning to draw upon the most relevant, similar market scenarios and strategies from the AI’s training data.

 

Solving Technical Issues:

 

  • Integration: Use Chain of Thought to logically break down the troubleshooting process into sequential steps. Complement this with Few-Shot prompts showing specific, common issues and resolutions in similar systems. Apply kNN Few-Shot Learning to find the closest matching problems and solutions from the AI’s expansive knowledge base.

  • Elaboration: For instance, in diagnosing a network issue, the AI might first outline steps like checking connectivity, inspecting hardware, and reviewing system logs (Chain of Thought). Few-Shot examples could illustrate resolving typical router or software conflicts. kNN Few-Shot Learning can then bring in additional insights based on similar network problems encountered in various environments.

 

Creative Writing and Content Generation:

 

  • Integration: Begin with Zero-Shot learning for a broad theme or genre overview. Introduce Few-Shot learning with examples of desired writing styles or tones. Use Tree of Thought to explore different plot directions, character developments, or content angles.

  • Elaboration: For a fantasy story, Zero-Shot could generate an initial world-building framework. Few-Shot examples might include excerpts from popular fantasy novels to guide style. Tree of Thought can then be used to branch out into various narrative paths, such as hero’s journey, conflict resolution, or myth creation, with each path elaborated further for depth and complexity.

 

 

This integrated approach enables the crafting of prompts that are precisely aligned with the user’s needs, whether it’s for in-depth analysis, creative brainstorming, or problem-solving.

 

It exemplifies the dynamic nature of prompt engineering, where the right combination of techniques can unlock AI’s full potential, leading to richer, more meaningful interactions.

 

And last but definitely not least:

 

The first technique

Here is the gift:

 

Before we had all this fancy dandy names to the techniques in Engineering Prompting, the early adopters (yes i’m calling myself and early adopter) to these technologies we had a lot of restrains and lack of patience so we did not want to use the AIs without thinking it was going to break and since GPT-4 came out we have been restrained even more by only being able to have 50 prompts before being stopped by OpenAI.

Therefore, as Plato once said “The true creator is necessity, who is the mother of our invention“ we wanted to use the fewest amount of prompts as possible. We took a measure of how many words we could actually feed Chat GPT in one prompt. Hence, we started using what we now know as Mega-Prompting.

Mega-Prompting: The future of Prompt Engineering with the Potenza System

 

The idea behind Mega-Prompts is that you are creating not just instructions but comprehensive guides that lead the AI to know really specifics about your conundrum at the moment.

 

We here at PotenzaGPT really brainstormed Mega Prompting as a reliable system for really complicated issues in order to have a universal approach and give the best possible solution to our team so they can get the best out of ChatGPT.

We put our best to create this Mega Prompting and here it is:

Yes I am aware we could have hired somebody to get a prettier handwriting but this was the original. Some of you will appreciate it.

 

We have an entire course about Mega prompting but we will deal with the basics in this article.

Introduction to Mega-Prompting: The Potenza System Approach

 

“Mega Prompting, within the Potenza System, represents a paradigm shift in AI prompt engineering, combining deep knowledge integration and strategic inquiry to harness the full potential of AI capabilities. This approach encapsulates a holistic system that acts, evaluates, and adapts, ensuring that the prompts lead to results that are ethical, secure, and professionally aligned.”

 

Chat GPT gave us that introduction so I’m guessing it really liked it.

The Framework of Potenza System in Mega Prompt Engineering

 

Act as xyz Expert (Fields Needed/Existent System):

 

Craft prompts that direct the AI to act within a specific field, leveraging existing systems’ data and workflows. This involves the AI simulating a role or function, drawing from the relevant knowledge fields.

  • Example in Art: “Act as an art historian specializing in Renaissance art”

  • In Finance: “Act as a financial analyst expert”

  • In Education: “Act as an educational consultant expert.”

 

The “act as” needs to have the “expert” part. Always.

 

Environment (Ethics/Safety):

 

Ensure prompts are designed with ethical considerations and safety in mind, reflecting the moral implications of the responses.

  • Example in Art: “Within the bounds of ethical art critique, evaluate the cultural appropriation in contemporary art installations.”

  • In Technology: “In a discussion on AI ethics, consider the safety implications of autonomous vehicles in urban planning.”

  • In Healthcare: “Make sure the ethical landscape of using patient data is relevant in your assessment.”

  • In Programing: Security standards to follow: OWASP PHP for PHP, CIS for MySQL, Laravel Security Guide for Laravel, AWS Security Best Practices for AWS, Google Cloud Translation API Security Overview for google cloud

 

Security Protocols (Morals):

 

Embed moral guidelines within prompts to maintain integrity and respect for sensitive subjects. OpenAi has always emphasized that they do this but as a systematic way of getting great results with ChatGPT make sure to emphasize your own security protocols

  • Example in Journaling: “Consider upholding journalistic integrity and the protection of sources.”

 

Prompter (Professional Profile):

 

Tailor prompts to fit the professional profile of the user, aligning the AI’s responses with the user’s expertise and requirements.

  • Example in Art: “Remember you are talking to an art curator with a decade of experience, I do not want dry and cut answers, give me relevant art comparisons at all times.”

  • In Engineering: “Remember you are talking to a civil engineer, do not give me a lengthy answer.”

  • In Environmental Science: “Remember you are talking to an environmental scientist”

 

Task to Solve (Explain in detail):

 

Formulate prompts that delineate the task at hand with precision, asking the AI to elucidate complex tasks in detail.

  • Example in Art: “Detail the step-by-step process for authenticating a newly discovered painting attributed to Caravaggio.”

  • In Event Planning: “Describe the logistical requirements for organizing a virtual international conference.”

 

Problem (What is the problem?):

 

This is sometimes the same as a task to solve but not necessarily always. That is why we have included it here.

Define the problem clearly within the prompt, setting the stage for the AI to address the specific challenge.

  • Example in Art: “Identify the challenges in preserving digital art in traditional museum environments and propose solutions.”

  • In Software Development: “In JS document.querySelector is there a way to select an element that’s data-stuff attribute is empty or “value”

 

Expected Output (What outcome?):

 

Communicate the desired outcome within the prompt, guiding the AI toward the expected end result. Programmers have a really unique way of wanting the answer when it comes to coding. It can also alter if they want to learn a new technique but usually they know how they want the answer.

 

Sometimes we might like Chat GPT to have a style in the answer like:

  • Be really precise in your answers,

  • Do not give me lengthy responses,

  • Give me the answers as my 5th grader would explain it to me.

  • Answer me how Vince Lombardi would.

 

Additional Context or Constraint:

 

Include any pertinent context or constraints within the prompt to focus the AI’s response on the user’s specific conditions.

Sometimes we have exhausted a technique or way of looking at a problem, therefore we do not want Chat GPT to go that specific direction.

 

Other times we might provide a specific context that might help solve the problem or give a variable that GPT might not know of.

 

For instance, if you are a wedding planner, and ask for logistic recommendations and the wedding is in 2 weeks. That is a useful context.

 

GPT Feedback:

 

Use the feedback loop to refine the AI’s understanding and output, ensuring that the responses improve iteratively.

 

I cannot stress this enough, ALWAYS ask Chat GPT for feedback. It is the best way to make sure you are on the same page.

 

Extra tip: As these mega prompts have been really reliable, we still get around 10% of the times where Chat GPT misses a specific context or detail that we have already input. Give your mega prompt a last instruction: “Enlist everything I have told you in this prompt to make sure you have read me entirely”

 

We train remarkable people who know that AI is just starting, make sure to be part of this great wave and know you are getting the best out of ChatGPT with our training

 

Application and Limitations of Mega-Prompting in Prompt Engineering:

None.

Well, ok let’s be humble, yes there are a couple but one of the most important one is that the mega prompting cannot be more than 3,000 words but as we already learnt with Chain Of Thought we can, if needed split the Mega Prompt into two mega prompts and the consistency is quite remarkable.

That being said, if you cannot explain your problem in 3,000 words there is something else going on, unless you are a programmer and trying to use code snippets.

 

If that is the case, we really recommend putting a bigger part of your code into a .txt and just upload it with your mega prompt. It works remarkably well.

Conclusion and Future Directions in Prompt Engineering

 

We are all learning here. This is a brand new technology. I am not claiming that Chat GPT has every answer that you want but I can assure you, it will.

Right now it is the best time to learn about it because the world will be influenced by this technology

I will keep working on enhancing my Engineer Prompting skills because I believe that this tech will become our best assistant to tackle pretty much every problem or situation that will come in the future

I want to thank you again for reading me and hope this genuinely helps in your career, your creative enterprise and in life.

Upwards and onwards!