Introductions
•Rename Yourself
•First Name
•Role
•T = Teacher
•A = Administrator
•C = Coach
•O = Other
•AEA = AEA
•Y or N if used ChatGPT
•Example: Aaron AEA Y
Resource Page and Slides https://bit.ly/AICSIN
Empowering Building Leaders with AI: Actionable Insights for Everyday Tasks
Agenda
How are you feeling about AI, REALLY?
Today we invite you to….
What is all this AI stuff?
What has happened?
Let’s remember AI is not new
November 30, 2022
ChatGPT was released for public use.
March 14, 2023
ChatGPT 4 was released for public use.
May 18, 2023
ChatGPT App
May 25, 2023
ChatGPT App available in 11 more countries
We need to question our assumptions about the way things are now.��They will not always be this way!
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AI Players
Google AI – Awareness, but not focus today
Google Examples
Google Examples
Google Examples
Reframe Thinking
HI + AI = New Path Forward���AI is a co-pilot NOT auto-pilot
Examine, Dismantle, Rethink, Redesign
Explore ChatGPT
Prompt Engineering Adventure
Four C’s of Using ChatGPT
Section 1
The Fundamentals of Prompt Engineering
The AI Classroom
Set the scene
Be clear and concise
Examples
Create an academic quiz
Read the following text and be prepared to answer questions on it
Create an outline of a presentation on…..
Grade this answer to this question and give reasons for your judgment
AI needs to know what needs to be accomplished and how to approach the work
Examples
You are a 5th grader
You are an academic professor of ……
You are an expert in the field of……
You are William Shakespeare. Answer all questions using the knowledge Shakespeare had and in his style.
Be clear, concise, and specific in expectations
Examples
Write three examples of a blog post title based on the following information……
Create 10 multiple choice questions using Bloom’s Taxonomy to make sure the questions develop a deeper understanding on the following content…..
This is the exam question……..This is the answer you are using to grade…. Provide a score and reasons for the following response.
Establish boundaries and clear expectations of output
Examples
Write in 100 words
Format in the style of a tweet
Write in British
Think About
Sample Work-Related Tasks
Let’s Practice
Section 2
Next level techniques and education focused
Zero-Shot vs Few-Shot Learning
Zero-Shot Learning
Few-Shot Learning
Zero-shot vs. few-shot learning
Sentiment Analysis:
Zero-shot vs. few-shot learning
Writing a Haiku:
Task: Write a haiku about a sunset.
A haiku is a form of Japanese poetry consisting of three lines with a 5-7-5 syllable pattern. ChatGPT would be expected to compose a haiku on the given topic, even if it hasn't seen specific examples of sunset haikus in its training data.
Writing a Haiku:
By providing two examples of haikus, ChatGPT is given context about the format and structure of the poetry before being asked to create a new haiku on the given topic.
One approach, called Chain of Thought prompting, gives the AI an example of how you want it to reason before you make your request, as you can see in the illustration from the paper.
Prompting Technique | Definition | Quality Prompt Example |
Zero-shot Prompting | Zero-shot prompting enables a model to make predictions about previously unseen data without the need for any additional training. It is used to generate natural language text without the need for explicit programming or pre-defined templates1. | "Write a short story about a brave astronaut." |
One-shot Prompting | One-shot prompting is used to generate natural language text with a limited amount of input data such as a single example or template. This can allow for the creation of predictable outputs from the large language model2. | "Given this example 'Once upon a time, there was a brave astronaut who saved the spaceship from disaster', write a short story about a brave astronaut." |
Few-shot Prompting | Few-shot prompting is a technique where the model is given a small number of examples, typically between two and five, in order to quickly adapt to new examples of previously seen objects. This technique can allow for the creation of more versatile and adaptive text generation models3. | "Given these examples '1. Once upon a time, there was a brave astronaut who saved the spaceship from disaster. 2. The brave astronaut courageously fixed the spaceship's engine, just in time', write a short story about a brave astronaut." |
Chain of Thought Prompting | Chain of Thought prompting is a recently developed prompting method that encourages the large language model to explain its reasoning. By showing the model some few-shot exemplars where the reasoning process is explained, the model will also show the reasoning process when answering the prompt. This often leads to more accurate results4. | "Given these examples with explanations '1. Once upon a time, there was a brave astronaut who saved the spaceship from disaster. This sentence introduces the protagonist and the conflict. 2. The brave astronaut courageously fixed the spaceship's engine, just in time. This sentence shows the resolution of the conflict', write a short story about a brave astronaut." |
Fine-tuning Prompting | Fine-tuning is a technique where a pre-trained model is further trained on a smaller, specific dataset to adapt the model to a particular task. This can be used to improve performance on specific tasks or to instill specific behaviors in the model. | "After fine-tuning the model with a collection of short stories about brave astronauts, prompt the model with 'Write a short story about a brave astronaut'." |
PROMPTING TECHNIQUE | DEFINITION | QUALITY PROMPT EXAMPLE | IMPACT ON OUTPUT ACCURACY |
Zero-shot Prompting | Zero-shot prompting enables a model to make predictions about previously unseen data without the need for any additional training. It is used to generate natural language text without the need for explicit programming or pre-defined templates | "Solve the equation: 2x + 3 = 7" | Since this is a straightforward problem and language models like GPT-3 have been trained on a variety of mathematical problems, the accuracy of the output would likely be high. However, for more complex problems, zero-shot prompting may not be as effective without additional context. |
One-shot Prompting | One-shot prompting is used to generate natural language text with a limited amount of input data such as a single example or template. This can allow for the creation of predictable outputs from the large language model | "Given this example 'Solving the equation 3x + 2 = 8 gives x = 2', solve the equation: 2x + 3 = 7" | Providing a solved example may guide the model to better understand the desired output format, potentially increasing the accuracy of the output. However, it's worth noting that the quality of the example provided is crucial in this case. |
Few-shot Prompting | Few-shot prompting is a technique where the model is given a small number of examples, typically between two and five, in order to quickly adapt to new examples of previously seen objects. This technique can allow for the creation of more versatile and adaptive text generation models【 | "Given these examples '1. Solving the equation 3x + 2 = 8 gives x = 2. 2. Solving the equation 4x - 2 = 10 gives x = 3', solve the equation: 2x + 3 = 7" | Providing multiple solved examples can improve the model's understanding of the task, possibly leading to more accurate and reliable outputs. |
Chain of Thought Prompting | Chain of Thought prompting is a recently developed prompting method that encourages the large language model to explain its reasoning. By showing the model some few-shot exemplars where the reasoning process is explained, the model will also show the reasoning process when answering the prompt. This often leads to more accurate results | "Given these examples with explanations '1. To solve the equation 3x + 2 = 8, subtract 2 from both sides to get 3x = 6, then divide by 3 to get x = 2. 2. To solve the equation 4x - 2 = 10, add 2 to both sides to get 4x = 12, then divide by 4 to get x = 3', solve the equation: 2x + 3 = 7" | As Chain of Thought prompting encourages the model to explain its reasoning, the output will likely contain both the solution and the steps to arrive at the solution. This can result in a better understanding of the problem-solving process and potentially higher accuracy, especially for more complex problems. |
Fine-tuning Prompting | Fine-tuning is a technique where a pre-trained model is further trained on a smaller, specific dataset to adapt the model to a particular task. This can be used to improve performance on specific tasks or to instill specific behaviors in the model. | "After fine-tuning the model with a collection of algebraic equations and their solutions, prompt the model with 'Solve the equation: 2x + 3 = 7'." | Fine-tuning the model on a specific task can significantly improve the accuracy of the output for that task. However, this approach requires a relevant dataset for fine-tuning and may limit the model's performance on tasks outside of the specific domain. |
Prompt Chaining
AI productivity benefits
Images for presentations
Fill out a form
Rewrite a document
Email Templates
Create new examples, problems, samples
Sample Prompts�From Zain Kahn
Learn any complex topic in just a few minutes:
Prompt: "Explain [insert topic] in simple and easy terms that any beginner can understand.”
Get ChatGPT to write in your style.�Prompt: "Analyze the writing style from the text below and write a 200 word piece on [insert topic]”
Train ChatGPT to generate prompts for you.�Prompt: "You are an AI designed to help [insert profession]. Generate a list of the 10 best prompts for your profession.
Ask ChatGPT to help you become better at using ChatGPT.�Prompt: "Create a beginner's guide to using ChatGPT. Topics should include prompts, priming, and personas. Include examples where necessary. The guide should be no longer than 500 words."lf. The prompts should be about [insert topic].”
Eliminate writer's block.�Prompt: "I'm writing a blog post about [insert topic]. I can't come up with a catchy title. Give me a list of 5 suggestions for the blog title for this piece.”
Generate new ideas.�Prompt: “I want to [insert task or goal]. Generate [insert desired outcome] for [insert task or goal]."
Try the following
Few Shot - give examples to follow
Provide it steps to follow
Incorporate your own ideas with the AI
Ask to explain it’s thinking
Push back
Expand on the second point
Have it ask you questions to better understand what you want
Try different tones, descriptors, roles, etc.
Create additional prompts or ask feedback on assignments
Let’s Practice Again
Try some new interactions
Section 3
AI layered tools
Word of Caution
Let’s Practice Again
Try some tools
Full Circle
What if instead of teaching our students to answers questions, we teach them how to question answers?
Transformative Potential of AI for Educators
Melanie Borden
…you can’t automate relationships
Examine, Dismantle, Rethink, Redesign
Perhaps a reframe of thinking. Instead of seeing tools as cheating we explore how learning how to prompt is to teach how to��� �
The future of education is human led, purpose driven, and technology augmented.��Dwayne Matthews
Thank you