The A-Z of Generative AI and ChatGPT - Chapter P

It’s hard to imagine that generative AI was barely a topic of discussion for organizations at the start of 2023, let alone the business and learning imperative it has become today.

But mark my words, generative AI will likely change much of what we do and how we do it, and it will LIKLEY do so in ways that no one is currently anticipating.

Success in the age of AI will largely depend on individuals' and organization’s ability to learn AI.

Why is Generative AI considered very important in that Age of AI?

Because even the most cursory of research shows that businesses and individuals who use generative AI are far more productive than those who don't.

'The opportunity to boost performance is astonishing: When using generative AI (in our experiment, OpenAI’s GPT-4) for creative product innovation, a task involving ideation and content creation, around 90% of our participants improved their performance. What’s more, they converged on a level of performance that was 40% higher than that of those working on the same task without GPT-4. People best captured this upside when they did not attempt to improve the output that the technology generated.' (Source BCG ).

AI skills are forecast to be the most highly-demanded skills of the next decade. But that doesn’t mean it’s obvious how a business or individuals should develop their skills and capabilities to take advantage of the most flexible application technology providers have ever built.

Getting your employees and your ready for AI will be the challenge. So, take your knowledge, productivity, employability, and business to the next level by reading my carefully curated series on Generative AI.?Grab a lifetime of learning in the?fastest time?possible and start reading. Let's begin with one of generative AIs biggest topics - prompt engineering .

Thank you for reading my latest article on AI and Generative AI risks. Here at LinkedIn and at Kieran Gilmurray I regularly write about AI, Data Analytics, RPA, and Intelligent Automation. To read my future articles simply join my network by clicking 'Follow'. Also feel free to connect with me via LinkedIn , Kieran Gilmurray ,?Calendly, Twitter , YouTube , Spotify ?or Apple Podcasts or read my latest book The A-Z of Organizational Digital Transformation to learn more.

To grasp the concept of prompt engineering, it is essential to first understand what prompts are and their role. Whereas traditional software is programmed, generative AI is trained, fine tuned, reinforced, and prompted . Prompts are the initial text inputs (instructions / directions / actions) given to a large language model, which it uses to generate responses or accomplish tasks. They can and do vary in nature, including text summarization, arithmetic problem-solving, and more commonly, question and answering.

What is prompt engineering?

Prompt engineering is the skill of crafting input queries (aka prompts) to elicit the most effective, accurate, and relevant responses from AI systems such as ChatGPT, Bing, or Bard.

With generative AI models, the output quality is directly influenced by how well your input prompts are designed. This discipline, therefore, becomes a bridge between human intent and AI capability, ensuring that the AI's responses align with your goals and needs.

Prompt engineering means figuring out how to ask the right question to get exactly the answer you need so you give to a machine learning model the best possible chance to get the output right. Inefficient prompts can lead to suboptimal outputs, wasting valuable time and resources.

• Example of a sub optimal prompt: Write a business plan for an AI start-up that helps lawyers.
• Example of a much better prompt: Can you provide a detailed business plan outline for starting an AI-powered legal tech company focused on streamlining case research and documentation for small to medium-sized law firms, including market analysis, potential challenges, revenue models, risks, and key technology requirements?

Mastering prompt engineering is not just about technical know-how; it's about understanding your specific needs and how to communicate them effectively to AI.

As AI continues to evolve, the role of prompt engineering in maximizing its potential becomes increasingly vital. Whether you're in legal tech or any other industry, the art of crafting the right prompt can be the key to unlocking the full power of AI in your business.

Will generative AI replace people?

The short answer is no but longer answer is that people who know generative AI, and can use AI in every aspect of their work, will replace those who do not.

How can prompt engineering help individuals ?

When your generative AI tool gets a strong prompt, it’s able to deliver a strong output. The stronger, more relevant the prompt, the better the end user experience. For example, generative AI can help individuals with

• Personalize Learning: Generative AI can tailor educational content to individual learning styles and paces, enhancing the effectiveness of learning experiences.
• Career Development: AI can suggest personalized career paths, training resources, and skill development opportunities based on individual interests and strength
• Financial Planning: AI can offer customized financial advice, including budgeting, investment strategies, and expense tracking, based on individual financial goals and habits
• Personal Assistants: AI-powered personal assistants can handle mundane tasks like booking appointments, answering emails, and managing to-do lists, freeing up time for more important activities.
• Content Creation: Instead of just consuming content, with generative AI anyone can now produce expert quality text, images or videos. For creative professionals, generative AI can offer inspiration, generate initial drafts, or suggest novel ideas, thereby augmenting the creative process.
• Accessibility: For individuals with disabilities, AI can enhance accessibility, with tools like speech-to-text, text-to-speech, and visual recognition technologies improving access to information and services.
• Mental Health Support: AI-powered apps can provide initial mental health support through guided conversations, mindfulness exercises, and mood tracking.

How can prompt engineering help teams?

Prompt engineering significantly benefits teams in a wide variety or ways. For example, generative AI can support teams to

• Automate Routine Tasks: Generative AI can handle repetitive and time-consuming tasks, freeing team members to focus on more complex and strategic work.
• Enhance Creativity: By providing new ideas, counter arguments, and perspectives, generative AI can aid in creative processes like content creation, design, and brainstorming.
• Improve Decision-Making: AI can analyse large datasets quickly and provide insights, helping teams make more informed decisions based on data rather than just intuition or gut feeling.
• Facilitate Personalized Customer Experiences: Generative AI can help tailor products, services, and communications to individual customer preferences, enhancing customer satisfaction and loyalty.
• Boost Productivity: By streamlining workflows and reducing the burden of mundane tasks, AI allows teams to be more productive and efficient in their core activities.
• Enhance Communication: Tools like AI-driven chatbots can assist in customer service and internal communication, ensuring quick and consistent responses.
• Provide Predictive Analysis: AI can forecast trends and outcomes based on historical data, aiding in strategic planning and risk management.
• Enable Rapid Prototyping: In fields like software development and product design, AI can rapidly generate prototypes, speeding up the innovation process.
• Facilitate Knowledge Management: AI can organize and retrieve information from large repositories, making it easier for teams to access and use corporate knowledge.
• Assist in Project Management: AI can help in scheduling, resource allocation, and monitoring project progress, ensuring that teams stay on track and efficiently manage their tasks.

How do you best integrate prompt engineering into your corporate strategy?

1. Invest in Training: To leverage this tool effectively, it's important for organizations to invest in training every member of their workforce in the nuances of prompt engineering. This includes understanding the AI model's capabilities and limitations as well as providing instruction and support to prompt well. So, equip yourself and your team with prompt engineering skills. This doesn’t necessarily require deep technical knowledge but an understanding of how to communicate effectively with AI.
2. Collaboration Between Teams: Prompt engineering thrives on diverse inputs. Encourage collaboration between technical teams and other departments e.g., business, IT and data science teams. Prompt engineering isn’t just a technical skill; it requires an understanding of the business context and objectives.
3. Establishing Best Practices: Develop best practices for prompt engineering within your organization. Create a culture of learning and adaptation around AI interactions. Document successful prompts and analyse the outcomes to refine your techniques. It should also include writing and communicating a security and ethics policy that provides clear do and don't guidance to all staff.
4. Monitoring and Evaluation: The AI landscape is dynamic. Continuously monitor the effectiveness of prompt engineering strategies and be open to adapt as AI technology evolves. From a business perspective, it is critical to gain significant, measurable productivity and profit gains from your investment in AI so wrap you finance team around your generative AI business plan.

According to Goldman Sachs , Generative AI could raise global GDP by $7 trillion (nearly 7%) and and boost productivity growth by 1.5 percentage points. The ecosystem could create markets for suppliers of technology and services worth hundreds of billions of dollars.

“[AI] will touch every sector, every industry, every business function, and significantly change the way we live and work,” said Pichai on stage in front of thousands of Google partners and customers at Google Cloud Next 2023

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Let me now provide you with some explanations of Generative AI terms beginning with P and some sample prompts to increase your productivity for each.

Each prompt guides ChatGPT to utilize specific Generative AI methods and principles for various applications, aiming to enhance the quality, relevance, and efficiency of its generated content.

1. Pre-Trained Models in Generative AI ??

Pre-trained models in Generative AI are neural networks that have been trained on vast amounts of data before being fine-tuned for specific tasks. It's like having an AI with a foundation of general knowledge, ready to specialize in various domains. Pre-trained models accelerate content generation by leveraging prior learning.

For Example: An AI-powered chatbot starts with a pre-trained language model, then fine-tunes it for customer support conversations specific to an e-commerce business.

Prompt: "ChatGPT, use pre-trained models as a foundation to enhance your understanding and generate contextually relevant replies to the following customer emails [copy customer emails text and some best practice response examples]."

2. Parallel Computing in Generative AI ??

Parallel computing in Generative AI involves breaking complex tasks into smaller sub-tasks that run simultaneously on multiple processors or devices. It's like teamwork, where many individuals collaborate to complete a project faster. Parallel computing speeds up content generation, making AI applications more efficient.

For Example: An AI research team uses a distributed computing system to train language models concurrently, reducing the training time.

Prompt: "ChatGPT, embrace parallel computing to optimize your content generation and respond swiftly to these user queries [copy user queries] in line with company brand values and tone [provide details of both to train ChatGPT]."

3. Privacy-Preserving Generative AI ??

Privacy-preserving Generative AI involves protecting user data and ensuring content generation without compromising personal information. It's like maintaining confidentiality while still offering personalized services. Privacy-preserving techniques enable AI models to generate content without storing or accessing sensitive data.

For Example: An AI-powered healthcare platform generates personalized health recommendations for users without accessing their medical records directly.

Prompt: "ChatGPT, make health recommendations based on the following patients medical records [link to existing medical records] and current symptoms [list symptoms].

4. Pruning in Generative AI ??

Pruning in Generative AI is a technique to remove unnecessary connections or parameters from neural networks. It's like trimming excess branches from a tree to enhance its growth. Pruning reduces model size and computation while maintaining content generation quality.

For Example: An AI language model undergoes pruning to remove redundant connections and optimize its performance.

Prompt: "Visualize the concept of 'Pruning in Generative AI' as an allegorical scene. In a vibrant digital landscape, depict a large, flourishing tree symbolizing a neural network. The tree's branches are dense with leaves and circuit-like patterns, representing the complex connections in AI.

Around the tree, a figure embodying 'Technology' is carefully trimming excess branches with tools resembling both gardening shears and futuristic tech gadgets. These trimmed branches, fading into digital particles, symbolize the removal of unnecessary connections in AI to enhance its efficiency and performance. The background should convey a sense of growth and optimization, with subtle hints of digital elements to emphasize the AI aspect".

5. Progressive Growing in Generative AI ??

Progressive Growing in Generative AI is a training approach where models start generating low-resolution content and gradually increase resolution complexity. It's like building a detailed painting by first sketching its basic outline. Progressive growing ensures smoother and more stable content generation.

For Example: An AI image generator progressively grows the resolution of images to create high-quality artwork.

Prompt: "ChatGPT, depict the concept of 'Progressive Growing in Generative AI' as an artistic process. Imagine a canvas split into several progressive stages. In the first stage, show a simple, rough sketch, symbolizing the initial low-resolution content generated by AI. As the image progresses from left to right, each subsequent stage reveals more detail and complexity, illustrating the gradual increase in resolution. The final stage should display a highly detailed and refined image, representing the culmination of the progressive growing process.

Around the canvas, an artist figure, embodying 'Artificial Intelligence,' is seen working on different sections, each with tools suited for that stage of refinement - from basic pencils to sophisticated digital tools. The background should subtly incorporate digital motifs and elements that suggest a blend of art and technology, emphasizing the AI's evolution in creating increasingly complex content."

6. Post-Editing in Generative AI ??

Post-editing in Generative AI involves refining or adjusting the output generated by AI models with human intervention. It's like having an AI writing assistant whose drafts are polished by an editor. Post-editing enhances the accuracy and coherence of content generation.

For Example: An AI language model generates draft reports, which are then refined by human editors before publication.

Prompt: "ChatGPT, post-edit the content that follows and provide refined, grammatically correct, and contextually accurate responses [copy and paste text into ChatGPT's command line]."

7. Policy Gradient in Generative AI ??

Policy Gradient in Generative AI is a reinforcement learning technique where models learn by trial and error, receiving feedback on the quality of their generated content. It's like training an athlete by providing rewards for successful actions. Policy gradient improves content generation by encouraging models to optimize their performance based on feedback.

For Example: An AI conversational agent improves its responses through reinforcement learning by observing user reactions.

Prompt: "ChatGPT, take this text ['copy text'] and having learned and been trained on prior user feedback [link to a database of feedback] improve your performance and write a new reply to the customers query."

8. Prompt Engineering in Generative AI ??

Prompt Engineering in Generative AI involves designing input instructions or queries to guide AI models' content generation. It's like providing a clear set of guidelines for a task. Prompt engineering enables users to obtain specific and desired outputs from AI models.

For Example: An AI language model generates poetry with different emotions based on prompts like "Write a joyful poem" or "Compose a sad poem."

Prompt: "ChatGPT, master the art of prompt engineering to generate content that aligns precisely with user preferences and instructions - here are my precise instructions - compose a deeply moving and sad poem that captures the essence of loss and longing. The poem should be a free verse with a melancholic tone, using vivid imagery to evoke a sense of sorrow. It should contain four stanzas, each with six lines. The first stanza should set the scene of a desolate landscape, perhaps a barren field in winter, symbolizing isolation. The second stanza should introduce a personal element, like the memory of a loved one who is no longer present, using metaphors of fading light or wilting flowers to convey the sense of loss. The third stanza should delve deeper into emotional reflection, expressing feelings of regret or unfulfilled wishes, perhaps using the imagery of an uncompleted journey or an unread letter. The final stanza should offer a glimmer of acceptance, yet retain the sombre mood, perhaps through a metaphor of a setting sun or closing curtains, indicating an end but also a sense of peace. Use language that is poignant and evocative, with a focus on natural elements to convey the themes of loss and longing."

9. Pragmatics in Generative AI ??

Pragmatics in Generative AI refers to understanding the implied meaning and context of language beyond literal interpretation. It's like recognizing sarcasm or humour in a conversation. Pragmatics enables AI models to generate contextually appropriate and socially intelligent content.

For Example: An AI language model interprets user queries based on pragmatics to respond more accurately in a casual or formal tone.

Prompt: "ChatGPT, embrace the subtleties of pragmatics to enhance your content generation, providing nuanced and context-aware responses to users. Now use that to reply to this user question [type question]"

10. Pseudo-Labelling in Generative AI ??

Pseudo-Labelling in Generative AI is a semi-supervised learning technique where AI models use their own generated data as additional labelled examples for training. It's like a student who creates their own quiz to reinforce learning. Pseudo-labelling helps AI models learn from their own outputs and improves content generation accuracy.

For Example: An AI image classifier generates labels for some of its training data and then uses these labels to improve its performance.

Prompt: "ChatGPT, visualize the concept of 'Pseudo-Labelling in Generative AI' as an educational scene. Picture a classroom setting where an AI, personified as a diligent student, is sitting at a desk surrounded by computers and digital equipment. The student is shown creating a quiz or a set of problems on a digital tablet, symbolizing the AI generating its own data. This tablet displays questions and answers, indicating the self-generated labeled examples. Around the student, there are several other 'AI students' working on the same quiz, representing the model using its own generated data for training. Each student's desk should have a progress bar above it, showing various levels of learning advancement, symbolizing the improvement in content generation accuracy. The walls of the classroom should be adorned with posters and charts related to AI concepts and learning, emphasizing the theme of education and self-improvement. The overall atmosphere should convey a sense of focus, innovation, and continuous learning, with a blend of traditional classroom elements and futuristic technology.."

Understanding the A-Z of Generative AI opens up a rich world of possibilities for business leaders. These concepts provide valuable insights into how AI can create new content, solve problems, and drive innovation across various industries. Embracing Generative AI can lead to enhanced creativity, improved decision-making and a competitive edge in the rapidly evolving data infused digital landscape.

Every single role you can think of will be impacted by Generative AI. In fact understanding how to interact with ChatGPT will soon be an essential key skill.? Below are 5 roles that begin with the letter P with an example prompt each massively boosting role productivity.

Project Manager. Project managers lead and oversee the planning and execution of projects.

• Prompt: Create a detailed project schedule with clear milestones and deadlines.
• Prompt: Conduct regular project status meetings to track progress and address any issues.

Public Relations Manager. Public relations managers handle an organization's communication with the public and media.

• Prompt: Develop a crisis communication plan to address potential reputation threats.
• Prompt: Coordinate a media event to launch a new product or service.

Product Marketing Manager. Product marketing managers develop marketing strategies for specific products.

• Prompt: Conduct market research to identify customer needs and preferences.
• Prompt: Develop a product launch plan with targeted marketing messages and channels.

Patent Attorney. Patent attorneys specialize in intellectual property law and assist clients with patent applications.

• Prompt: Conduct a patent search to determine the uniqueness of a client's invention.
• Prompt: Prepare and file a patent application for a new invention.

Psychologist. Psychologists study behaviour and mental processes and provide counselling or therapy to individuals.

• Prompt: Conduct a comprehensive psychological assessment for a client to inform treatment planning.
• Prompt: Develop personalized coping strategies for clients dealing with stress or anxiety.

Some of the best Generative AI articles from some of the best sources on the internet.

• BCG How People Create and Destroy Value with Generative AI
• ERP Today?Generative AI for the enterprise ?-?Pascal BORNET ?and?Kieran Gilmurray
• OpenAI Prompt engineering - OpenAI API
• SSRN: Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
• BCG AI at Work: What People Are Saying
• BCG Responsible AI is Crucial to Avoid New AI Tech Risks ?

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