- Should prompt engineering be part of computer science and IT curriculums in high school and college? Why or why not?- What are the potential benefits and risks of teaching students prompt engineering? Could it help prepare them for careers in AI or lead to irresponsible use of language models?- How might prompt engineering classes be structured? Should they focus more on the technical side or the ethical implications?- At what age would it be appropriate to introduce prompt engineering concepts? Does the level of abstraction and complexity need to be adjusted for different grade levels?- Do you think most students today understand how AI systems like ChatGPT work? Should prompt engineering help improve their AI literacy?- Could lessons in prompt engineering help students think more critically about how to interact with AI?- What biases and limitations could be perpetuated if students don't learn responsible prompt engineering?- Should prompt engineering be optional or required? Is there a risk of leaving some students behind if they don't receive this knowledge?- How can teachers who are unfamiliar with AI learn enough to teach prompt engineering themselves? Do schools need to hire specialized instructors?- What ethical guidelines and guardrails need to be put in place for student prompt engineering projects?
Prompt engineering is an emerging field that will shape the future of artificial intelligence. As AI becomes more ubiquitous, understanding how to create effective prompts to get useful results from AI systems will be a crucial skill. That's why teaching the fundamentals of prompt engineering as early as elementary school would provide students with a strong foundation in this critical domain.
In elementary grades, students should be introduced to the basic concepts behind prompting AI systems. Simple writing exercises focused on phrasing requests clearly and giving enough relevant context would familiarize young students with foundational prompt engineering skills. AI ethics and safety could also be woven into elementary curriculum to establish considerate prompt engineering habits from an early age.
By middle school, prompt engineering coursework could cover more technical skills like learning what types of information AI systems need to generate high-quality outputs. Students could start experimenting with different prompt formats and data inputs to see how they influence AI performance. Studying real-world examples of effective business and research prompts would provide insight into practical applications.
In high school, full-semester prompt engineering classes could teach advanced skills like tailoring prompts for specific AI architectures, adjusting prompts iteratively, and using techniques like chaining and demonstrations. Dedicated lab time to test prompts on various AI systems would give students hands-on practice. Statistical analysis of prompt performance could also be introduced to quantify prompt quality.
Laying this early groundwork in prompt engineering will allow students to refine their skills further in higher education. Colleges could offer specialized prompt engineering degrees that combine computer science, linguistics, psychology, and ethics. With the right curriculum starting in elementary school, the prompt engineers of tomorrow will enter the workforce ready to unlock the full potential of AI safely and responsibly.
Advances in AI like textual inversion and generative adversarial networks have exploded, with systems like GPT-3 and DALL-E 2 producing remarkably intelligent outputs. This rapid progress means demand is soaring for AI specialists able to expertly formulate textual prompts to yield useful machine-generated content, creative media, and more.
In this environment, prompt engineering has emerged as a crucial skill for harnessing AI's potential. And prompt engineers able to "speak" fluently to language models like GPT-3 are set to have incredible career opportunities. That's why educators must start adapting curricula now to prepare students.
The Core Skills of Prompt Engineering
Prompt engineering combines creativity and technical precision. Engineers must communicate desired outputs clearly while allowing flexibility for AI varitation and innovation. Core skills include:
- Understanding AI model architectures/datasets to guide prompt formulation
- Framing requests creatively focused on intended utility
- Outlining detailed requirements tailored to AI strengths/limitations
- Providing diverse input content to enrich context
- Testing and iterating prompts for continuous improvement
These skills bridge technical and artistic domains, making prompt engineering an interdisciplinary pursuit.
Teaching Prompt Engineering Fundamentals
While prompt engineering leans on emerging AI, many foundations should be taught in schools today:
- Creative writing - skills for engaging descriptive settings and scenarios
- Research and analysis - assessing data/content to inform prompt creation
- Programming - basic coding ability to manipulate AI inputs/outputs
- Design thinking - ideating solutions by synthesizing constraints
- Critical thinking - evaluating results to refine prompts
Building real-world prompt engineering practice into coursework across subjects develops these talents even before students directly interact with AI systems.
By high school or college, students should gain access to AI labs for direct prompt engineering lessons. Here they can be challenged to create prompts that solve problems, reflect creative visions, yield useful content/media assets, and more while interacting with systems like GPT-3 firsthand.
Post-Education Opportunities
Once equipped with multifaceted prompt engineering abilities, exciting career paths unfold for graduates. Roles may include:
- AI Content Strategists - Develop prompts for marketing communications, journalism, entertainment and educational media
- AI Creative Directors - Guide prompt engineering for advertising, video production/post-production and design projects
- AI Solutions Architects - Construct prompts to build helpful tools, services and data-driven solutions
- Autonomous Machine Learning Engineers - Automatically generate code and data by prompting AI assistants
- AI Legal/Ethics Specialists - Formulate prompts adhering to laws, corporate policies and societal standards
And as organizations increasingly incorporate AI, demand for prompt engineering skills will proliferate across industries.
The Future Is Prompt Engineering
Forward-looking schools should prioritize prompt engineering today. With curriculum updates, technological investments and real-world projects, educators can develop versatile prompt engineers ready to unlock AI's vast potential while avoiding its risks. It's an exciting frontier requiring creative, analytically-minded talent that schools must start cultivating. The future of work depends on it. Here is an outline of what a prompt engineering curriculum could look like across elementary, middle, and high school grade levels:
Elementary School:
- Basics of giving instructions - Structure prompts as requests, provide context, give clear directions.
- Creative writing - Develop storytelling skills and descriptive scenarios to translate ideas into words.
- Coding/sequencing fundamentals - Introduce following & arranging instructions to achieve outcomes.
- Group collaborations - Practice articulating ideas and compromising to create prompts together.
- Ethics basics - Discuss being responsible and helpful when requesting AI to generate content.
Middle School:
- Writing craft - Refine abilities to vividly describe settings, characters, and plots that AI could depict.
- Coding languages - Start Python, JavaScript, or other languages to arrange instructions and data.
- AI fundamentals - Explain how AI models work at a basic level so students start gaining intuition.
- Research skills - Instruct assessing sources/information to inform prompt creation.
- Ethics discussions - Debate potential societal impacts of AI like bias in data and automation.
High School:
- Creative writing - Develop complex, thematically-rich prompts for AI scene/character/story generation.
- Coding projects - Have students configure datasets and train basic machine learning models.
- AI architecture - Explain state-of-the-art language models like GPT-3 so students understand their capabilities.
- Analysis & testing - Teach scientifically iterating prompts for improved results.
- Ethics simulations - Role play scenarios on AI risks to hone judgment.
The key is introducing foundational prompt engineering skills steadily across grade bands - creative ideation, coding, AI mechanics, critical thinking and ethics. Scaffold so by high school, students can actively demonstrate skills, like using GPT-3 for complex creative projects that weigh ethical implications. This builds readiness for emerging roles in AI content development, product design, autonomous engineering and beyond.
Getting the Most Out of Your Text to Art Prompts
Text to art generators like MidJourney and DALL-E are revolutionary AI systems that can create stunning images and videos from text descriptions. But coming up with the right prompts can be challenging. This guide will teach you how to write text prompts that help these systems generate their most creative, detailed, and inspiring results.
Understand How the AI Works
The key to prompting text to art AIs is understanding a little about their training process. Systems like MidJourney and DALL-E are trained on vast datasets pairing text captions and descriptions with images and videos. By entering a new text prompt, you give clues about what kind of image or video you want the AI to generate based on these correlations in its training data.
So prompts that are most similar to the captions used to train these AIs will produce the best results by giving the clearest signal of what you’re looking for. Keep this in mind as you craft your prompts. Leaning into the capabilities and limitations baked into the training data helps enormously.
Get Specific
Don’t be subtle or vague in your prompts. These AIs interpret words very literally, so capturing precise details is important. Include descriptions like:
- Subject matter/content: What is being depicted? Name the key subject(s), themes, and setting.
- Style: Is it realistic, abstract, cartoonish? Name a specific genre or art movement if applicable.
- Composition: How is the scene framed and oriented? Close-up? High angle? Portrait vs landscape?
- Lighting & ambience: Time of day? Bright or dim? Sunny or moody?
- Fine details: Colors, shapes, facial expressions, clothing details, etc. The more the better.
Prompt formatting like "A beautiful oil painting of a red flower in a blue vase on a window sill, with the soft glow of morning light streaming in" tells these systems exactly what you're looking for.
Try Unusual Blends
Part of the magic of text-to-art AIs is their potential for mixing and matching disparate concepts, styles, and genres in the same image or video. Bring together ideas they don’t often see paired:
- Hybrid creatures like an armored gorilla or flower elephant
- An astronaut riding a galloping horse through space
- A baby interpretive dancing with a robot
Juxtapose incongruous elements that catch the imagination and give the AI creative license to connect the dots.
Guide Not Gatekeep
Using overly narrow and strict prompts can unintentionally limit the AI’s inventiveness. Phrases like “only”, “exactly”, “nothing else”, and listing what not to include signals to the AI to play it safe and create something literal rather than push its imagination.
Instead, positively guide and encourage the AI’s creativity by keeping prompts more open-ended. You can always refine based on the results. Let the AI show its stuff!
Try Photo Bashing
Both MidJourney and DALL-E allow "photo bashing", mixing elements from multiple photographs together into a collage for manipulation. Uploading source images along with descriptive text about what to crossbreed or edit helps steer the AI.
For example, upload a face with a prompt about surrounding it with a starry galaxy and supernova explosions. Or blend animals, objects, landscapes and more. Give the AI raw materials to work its magic.
Refine Gradually
Getting perfect results straight away is rare. Instead, be prepared to gradually refine your text prompts through multiple generations, building on what does and doesn't work.
Adjust descriptors, swap stylistic elements, remove parts that get overemphasized or just aren't working. Text prompts are hypotheses so experiment iteratively using the AI’s output to guide adjustments.
Pay attention to quirks, stand out details, and surprises in the generated images/videos for inspiration. Often the AI “gets it wrong” in wonderfully creative ways you can double down on for intriguing results.
Soon you’ll zero in on wonderfully imaginative creations through this collaborative evolution bridging what you asked for and how the AI interprets it.
That covers the key tips and strategies for writing text prompts that help you get the most out of incredible AI platforms like MidJourney and DALL-E. Formulate prompts focused, specific and yet creatively open-ended. Describe unusual blends of concepts and subjects. Provide source images for manipulation. Then iteratively refine based on the outputs, collaborating with the AI to guide it towards your vision. Follow these principles, and you’ll be blown away by the worlds you imagine into creation.
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