Prompt Library (Prompts that you can use with your clubs)
What is Generative AI?
Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, or code. Generative AI uses various techniques, such as deep learning, natural language processing, computer vision, and generative adversarial networks, to learn from existing data and generate novel and realistic outputs. Generative AI has many applications and benefits, such as enhancing creativity, improving data quality, augmenting human capabilities, and solving complex problems.
What Generative AI is not
Generative AI is a branch of artificial intelligence that aims to create new content or data from scratch, such as images, text, music, or code. However, generative AI is not a magic tool that can produce anything you want without any constraints or limitations. There are some common misconceptions about what generative AI can and cannot do, such as:
Generative AI is not a replacement for human creativity or expertise. It can only generate content based on the data it is trained on, and it cannot understand the meaning, context, or quality of the output. Human feedback and evaluation are still essential to ensure the relevance and appropriateness of the generated content.
Generative AI is not a guarantee of originality or novelty. It can only recombine existing elements from the data it is trained on, and it cannot invent new concepts or ideas that are not present in the data. There is always a risk of plagiarism or duplication when using generative AI, especially if the data is not diverse or comprehensive enough.
Generative AI is not a solution for all problems or domains. It can only generate content that is compatible with the data it is trained on, and it cannot handle tasks that require logic, reasoning, or common sense. Some domains, such as scientific research, legal documents, or medical diagnosis, may not be suitable for generative AI due to ethical, legal, or safety concerns.
Intellectual Property Rights: Who owns the content generated by AI? Is it the creator of the AI model, the user who provided the input, or the organization that owns the AI system? This raises complex legal and ethical questions about copyright and ownership.
Bias and Discrimination: AI models can inadvertently learn biases present in the training data. This can lead to the generation of content that reinforces stereotypes or discriminates against certain groups. Ensuring fairness and avoiding bias in generative AI is a significant ethical challenge.
Privacy Concerns: Generative AI models can be used to create deepfakes or synthetic media that convincingly mimics real individuals. This can be used maliciously to deceive, manipulate, or violate the privacy of individuals.
Economic Impact: The automation of creative tasks by AI might lead to job displacement in creative industries. Ethical considerations must be made regarding the potential economic impact on artists, writers, musicians, and other creative professionals.
Authenticity and Originality: The ease with which AI can generate content raises questions about the value of human creativity and originality. What distinguishes human-made art from AI-generated art? How do we define authenticity in a world where machines can mimic human creativity?
Accessibility and Inclusion: While generative AI can democratize access to creative tools, it also raises questions about who has access to these technologies and how they are used. Ensuring that AI does not exacerbate existing inequalities is an important ethical consideration.
Misuse and Malicious Intent: Generative AI can be used for harmful purposes, such as generating misinformation, propaganda, or offensive content. The ethical use of these technologies requires robust governance and oversight.
Environmental Impact: Training and running large-scale generative models can consume significant computational resources, leading to substantial energy consumption. The environmental impact of AI is an emerging ethical concern.
Transparency and Accountability: Understanding how and why a generative AI model produces specific outputs can be challenging. Ensuring transparency in the decision-making process and holding entities accountable for the consequences of AI-generated content is vital.
Community and Cultural Sensitivity: Generative AI must be mindful of cultural nuances and sensitivities. Thoughtless generation of content that might be offensive or inappropriate to certain cultures or communities must be avoided.
Examples of Generative AI Platforms
It’s worth noting that the field of generative AI is rapidly advancing, so new platforms and improvements on existing platforms continue to emerge regularly and this list may soon become outdated.
OpenAI’s GPTseries (likeChat GPT-3andChat GPT-4): Widely known for natural language processing tasks, these models can generate coherent and contextually relevant text based on given prompts.
DALL·E by OpenAI: An AI system that can generate unique images from textual descriptions.
AI Art Prompt Writing Tool Promptomania: A more advanced visual prompt editor that can help you create a good AI image prompt when you want more options and control over the prompt building process
DeepArt: Uses neural networks to turn your photos into artwork based on different styles.
DeepDream by Google: Originally developed to give a sense of what deep learning neural networks see when they look at images, it can produce psychedelic and intricate images.
Magenta by Google: An open-source research project that explores the role of machine learning as a tool in the creative process, especially in generating music and art.
RunwayML: Offers a toolkit that allows creators to use AI in various ways, including generating images, videos, and more.
Getting the Best Results from the AI
What are Prompts?
Prompts are initial input statements or questions that guide the AI’s output generation. They serve as a starting point, providing direction for the AI to generate a response or content.
Are you ready to give prompting a try? Try our Kiwanis-specific prompts here
Here’s a breakdown of how prompts function in generative AI:
Context Setting: A prompt establishes the context for the AI’s response. For instance, asking “Describe the process of photosynthesis” provides a specific context for the AI to generate a detailed explanation about photosynthesis.
Task Definition: Prompts can specify the kind of task the AI should perform. For example, “Translate the following English text to French:” sets the task for the AI to perform translation.
Tone and Style Guidance: Prompts can also indicate the desired tone or style of the response. If you prompt with “Write a poem about the moon,” the AI is directed to generate poetic content about the moon, rather than a scientific explanation.
Controlled Outputs: By tweaking prompts, users can guide the AI to generate more refined or specific outputs. For example, by specifying the format, like “List three main causes of…,” users can obtain a bulleted list instead of a lengthy paragraph.
Generative AI models, especially large models like GPT series from OpenAI, do not have explicit instructions for every specific task coded into them. Instead, they rely heavily on the prompts they’re given to determine what kind of content to produce. They’ve been trained on vast amounts of data, learning patterns and information from that data. Prompts serve as a way to tap into this learned knowledge and guide the generation process.
Given the importance of prompts in shaping the AI’s response, there’s a growing interest in understanding and crafting effective prompts to optimize and control the outputs of generative models.
Creating Good Prompts
Creating good prompts for generative AI models, especially models like ChatGPT-4, is essential for good results. here are some tips for writing good prompts:
Be specific: For instance, instead of saying, “Create a social media campaign,” you can write, “Create a social media campaign for an ecommerce website that sells graphic T-shirts for fans of movies and comics like Star Wars, Harry Potter, and the Marvel Cinematic Universe.”
Take a conversational tone: Avoid jargon, slang, or complex phrases the AI may not understand. Write like you’re talking to a colleague, not a computer.
Use open-ended questions: Avoid yes or no questions, which limit the AI’s ability to provide more detailed information.
Set a persona: Ask the AI to give answers from the perspective of someone well-known, like Albert Einstein, Stephen King, Ernest Hemingway or Oprah Winfrey — or a specific type of person or persona like a middle manager or a demanding customer.
Define your audience and channel: If you’re writing for Gen Z or middle-aged dads, specify that in your prompt. Are they reading on a social media platform like Twitter or LinkedIn, a blog post, or on a store’s website? Give that information as well.
Ask follow-up questions: If you’re not satisfied with the initial response, ask follow-up questions to get more information. This is also known as “prompt chaining,” where you break up your prompts to get more concrete and customized answers — and use answers from one prompt to elicit the next.
Use a prompt building tool: Especially for image creation, there are AI prompt writing tools that can help you write a good prompt.
Remember, writing good prompts is an art and science in itself. As generative AI models become more integral to various industries and daily life, the ability to communicate effectively with these models through well-designed prompts becomes increasingly important.
Examples of Good and Bad Prompt Writing
The quality of a prompt often depends on the clarity and specificity of the intended task. Here are some examples across different domains:
General Knowledge:
Poor Prompt: “Tell me about war.”
Good Prompt: “Provide a summary of the Cold War.”
Creative Writing:
Poor Prompt: “Write a story.”
Good Prompt: “You are the famous author George H. Martin. Compose a short story about a dragon who’s afraid of heights.”
Image Creation:
Poor Prompt: “Create an image of a wine glass on a table with a dog.”
Good Prompt: “Create an image by piet mondrian, digital art advertising poster of in the style of constructivism that has a wine glass filled with red wine resting on a small round rustic wooden table with a small dog sitting on the floor next to the table looking up at the wine glass.”
Scientific Explanation:
Poor Prompt: “What’s that thing plants do?”
Good Prompt: “Explain the process of photosynthesis in simple terms.”
Translation:
Poor Prompt: “What’s this in another language?”
Good Prompt: “Translate the following English sentence into Spanish: ‘The weather is lovely today.'”
Mathematical Problem:
Poor Prompt: “Calculate a good average for the following numbers: 10,23,344,21,10”
Good Prompt: “Calculate the Winsorized mean for the following numbers: 10,23,344,21,10.”
Historical Inquiry:
Poor Prompt: “Tell me about olden times.”
Good Prompt: “Describe the significance of the Magna Carta in the context of medieval England.”
Advice or Recommendations:
Poor Prompt: “What should I watch on TV?”
Good Prompt: “Recommend three critically acclaimed science fiction movies from the 21st century.”
Technical How-to:
Poor Prompt: “How do I make a WordPress website?”
Good Prompt: “Provide a step-by-step guide for setting up a basic WordPress website.”
Mood or Tone Specification:
Poor Prompt: “Write a story about a forest.”
Good Prompt: “Write a short story that describes an enchanted forest with a mysterious creature that helps two lost children find their way back to their home.”
Philosophical or Abstract Queries:
Poor Prompt: “Tell me about how thinking works.”
Good Prompt: “Discuss the concept of consciousness from a philosophical perspective.”
The more precise and clear a prompt is, the more likely it is to elicit an informative, relevant, and accurate response from the AI. However, it’s also worth noting that sometimes, particularly in creative tasks, leaving a prompt intentionally open-ended can produce a wide range of intriguing outputs. The “goodness” of a prompt can thus also depend on the desired outcome—whether you seek specificity, creativity, breadth, or depth.
Kiwanis-specific Prompt Example
Service Club Ideas
Bad Prompt: “Provide me with a service project idea.”
Good Prompt: “You will play the role of an ideator. Suggest a service project for my Kiwanis club. Ask me for 1 noun and remember it. Then you will select 2 random nouns from a random list of 100 nouns. You will then create a service project based on the 2 random nouns you have selected and the 1 noun that I provided. Remember you will not reveal your 2 nouns until after I provided you with my noun. Then create a simple graphical logo that I can use with the project”
Business Writing
Bad prompt: “Create an email message for our upcoming Wine and Wags event”
Good prompt: “Create text for an email message that I can send to prospective service providers and vendors to get them engaged and interested in contributing to the project.
Project Name: “Wine and Wags”
Project Description: “A fundraiser to benefit children patients at Riley Children’s hospital that will help pay the expenses of families associated with long-term hospitalization costs like family lodging and food expenses. The event will bring in dogs from the local animal shelter Brighter Days in an auction-style format where qualified bidders bid on which dogs they’d like to adopt or even sponsor. The money collected will go to Riley Hospital and the Brighter Days shelter. In addition, there will be local wineries offering tastings that will bring in additional money that will also be shared between Riley Hospital and the animal shelter Brighter Days
Event Day & Time: Saturday, August 9th 2023
Event Location: Wayne County Community Center, Detroit Michigan (https://www.waynecommunitycenter.org)
For more information contact: Kelly Kiwanian [email protected] (800)222-3333
AI Resources
Prompt Building Tools
What is Generative AI?
Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, or code. Generative AI uses various techniques, such as deep learning, natural language processing, computer vision, and generative adversarial networks, to learn from existing data and generate novel and realistic outputs. Generative AI has many applications and benefits, such as enhancing creativity, improving data quality, augmenting human capabilities, and solving complex problems.
What Generative AI is not
Generative AI is a branch of artificial intelligence that aims to create new content or data from scratch, such as images, text, music, or code. However, generative AI is not a magic tool that can produce anything you want without any constraints or limitations. There are some common misconceptions about what generative AI can and cannot do, such as:
Ethical Considerations when using AI
Examples of Generative AI Platforms
It’s worth noting that the field of generative AI is rapidly advancing, so new platforms and improvements on existing platforms continue to emerge regularly and this list may soon become outdated.
Getting the Best Results from the AI
What are Prompts?
Prompts are initial input statements or questions that guide the AI’s output generation. They serve as a starting point, providing direction for the AI to generate a response or content.
Are you ready to give prompting a try? Try our Kiwanis-specific prompts here
Here’s a breakdown of how prompts function in generative AI:
Generative AI models, especially large models like GPT series from OpenAI, do not have explicit instructions for every specific task coded into them. Instead, they rely heavily on the prompts they’re given to determine what kind of content to produce. They’ve been trained on vast amounts of data, learning patterns and information from that data. Prompts serve as a way to tap into this learned knowledge and guide the generation process.
Given the importance of prompts in shaping the AI’s response, there’s a growing interest in understanding and crafting effective prompts to optimize and control the outputs of generative models.
Creating Good Prompts
Creating good prompts for generative AI models, especially models like ChatGPT-4, is essential for good results. here are some tips for writing good prompts:
Remember, writing good prompts is an art and science in itself. As generative AI models become more integral to various industries and daily life, the ability to communicate effectively with these models through well-designed prompts becomes increasingly important.
Examples of Good and Bad Prompt Writing
The quality of a prompt often depends on the clarity and specificity of the intended task. Here are some examples across different domains:
General Knowledge:
Creative Writing:
Image Creation:
Scientific Explanation:
Translation:
Mathematical Problem:
Historical Inquiry:
Advice or Recommendations:
Technical How-to:
Mood or Tone Specification:
Philosophical or Abstract Queries:
The more precise and clear a prompt is, the more likely it is to elicit an informative, relevant, and accurate response from the AI. However, it’s also worth noting that sometimes, particularly in creative tasks, leaving a prompt intentionally open-ended can produce a wide range of intriguing outputs. The “goodness” of a prompt can thus also depend on the desired outcome—whether you seek specificity, creativity, breadth, or depth.
Kiwanis-specific Prompt Example
Service Club Ideas
Business Writing
Video of the Service Club Ideation Example
[DEMO VIDEO COMING SOON!]
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