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AI models can generate incorrect or nonsensical information—known as hallucinations—along with sometimes subtle but meaningful factual errors, or biased and inappropriate statements. Closely monitoring outputs is essential to identify and mitigate these issues, helping maintain the integrity of information and ensuring any problematic content can be caught and addressed promptly.

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AI systems often process user data, which can include personal or confidential details. Managing privacy settings—such as limiting data sharing, enabling anonymization, or reviewing access permissions—helps protect sensitive information, prevent unauthorized exposure, comply with regulations like GDPR or CCPA, and reduces risks of data breaches or misuse.

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Do use AI for accessibility checks (e.g., "Check this content for WCAG compliance")

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Do use AI for accessibility checks (e.g., "Check this content for WCAG compliance")

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AI can efficiently analyze content for accessibility issues, such as missing alt text for images, poor color contrast, or navigation barriers, based on standards like WCAG (Web Content Accessibility Guidelines). This promotes inclusivity, making resources usable for everyone, including people with disabilities, and helps avoid legal or ethical oversights in content creation.

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The System Prompt Library is envisioned as a dynamic, user-centered digital repository designed for educators. Its primary purpose is to provide a comprehensive collection of customizable prompts for Generative Pre-trained Transformers (GPTs), tailored specifically for educational use.

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GitHub - ncwilson78/System-Prompt-Library: A library of shared system prompts for creating customized educational GPT agents.

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A library of shared system prompts for creating customized educational GPT agents. - ncwilson78/System-Prompt-Library

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The Prompt Library is organized into categories for Instructor Aids, Student Exercises, and Other Prompts.

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Prompt Library

Prompts on this page (but no other content on the site) are licensed under Creative Commons License Attribution 4.0 International This license requires that reusers give credit to the creators (Ethan Mollick and Lilach Mollick). It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes. Use prompts at your own risk, outputs may not be correct.

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Prompt Library - More Useful Things: AI Resources

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Prompts on this page (but no other content on the site) are licensed under Creative Commons License Attribution 4.0 International This license requires that reusers give credit to the creators (Ethan Mollick and Lilach Mollick). It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.

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Don’t input any personally identifiable information.​

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Don’t input any personally identifiable information.​

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AI interactions are not inherently private, and data may be stored, logged, or subject to breaches. To protect privacy, never share personally identifiable information (PII) such as full names, addresses, phone numbers, ID or Social Security numbers, or anything that could be used to uniquely identify or locate an individual.

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Sensitive information includes—but is not limited to—financial records, health information, security credentials, and passwords. Sharing these details with AI systems can expose individuals or organizations to unnecessary risks. AI tools are not designed to securely handle or protect sensitive data, and misuse could result in financial, legal, or personal harm.

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Don’t input any university intellectual property (IP)

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Don’t input any university intellectual property (IP)

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Intellectual property (IP), such as research findings, grant proposals, unpublished manuscripts, proprietary data, etc. should never be entered into AI platforms. Many AI systems train on or store user inputs, which could result in unintended disclosure or unauthorized use. Protecting university IP is both an ethical and legal responsibility.

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Don’t rely on AI for legal or medical advice without verification

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Don’t rely on AI for legal or medical advice without verification

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AI-generated output may contain inaccuracies, outdated information, or oversimplifications. It should never replace professional guidance in areas with serious personal or legal consequences, like healthcare or law. If AI is used to gather background or clarify concepts, always cross-reference the information with authoritative sources or validate it through qualified professionals.

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This is a technique where you guide an AI model to break down its reasoning process into explicit steps, rather than jumping straight to an answer. By instructing the AI to "think step by step," it mimics human-like reasoning, which often leads to more accurate results, especially for complex problems involving logic, math, or multi-step decision-making.

Why it works: LLMs (large language models) are trained on vast data, but they can sometimes make errors in intricate tasks. CoT helps by forcing the model to articulate intermediate thoughts, reducing mistakes and improving transparency.

Example: Consider this logic puzzle: "There are three switches, but only one turns on the light in the next room. The other two do nothing. One label says 'ON', one says 'OFF', and one is blank. All labels are wrong. Which switch turns on the light?"

Without CoT: You might prompt: "Which switch turns on the light?"

With CoT: "Reason step by step to figure out which switch turns on the light: There are three switches, but only one turns on the light in the next room. The other two do nothing. One label says 'ON', one says 'OFF', and one is blank. All labels are wrong."

The AI would respond something like:

1. The puzzle likely intends for the switches to have functions: one turns the light on, one turns it off, and one does nothing, even though it says "the other two do nothing" (perhaps meaning they don't turn it on).

2. The labels are 'ON' (meaning turns on), 'OFF' (meaning turns off), and blank (meaning does nothing or neither). All labels are wrong, so each switch is not what its label claims.

3. The blank switch cannot do nothing (because that would make the "blank = nothing" correct), so it must turn off the light (it's labeled blank but is actually off, making the label wrong).

4. The 'ON' switch cannot turn on the light (label would be correct), so it must do nothing (labeled on but does nothing, wrong).

5. The 'OFF' switch cannot turn off the light (label would be correct), so it must turn on the light (labeled off but turns on, wrong). So, the switch labeled 'OFF' turns on the light.

This approach has been shown in research to boost performance on benchmarks like arithmetic or commonsense reasoning.

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Chain-of-Thought (CoT) Prompting

Image Source: Wei et al. (2022) (opens in a new tab)

Introduced in Wei et al. (2022) (opens in a new tab) , chain-of-thought (CoT) prompting enables complex reasoning capabilities through intermediate reasoning steps. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before responding.

The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False. The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24. A: Adding all the odd numbers (17, 19) gives 36. The answer is True. The odd numbers in this group add up to an even number: 16, 11, 14, 4, 8, 13, 24. A: Adding all the odd numbers (11, 13) gives 24. The answer is True. The odd numbers in this group add up to an even number: 17, 9, 10, 12, 13, 4, 2. A: Adding all the odd numbers (17, 9, 13) gives 39. The answer is False. The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.

Wow! We can see a perfect result when we provided the reasoning step. In fact, we can solve this task by providing even fewer examples, i.e., just one example seems enough:

The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False. The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.

Keep in mind that the authors claim that this is an emergent ability that arises with sufficiently large language models.

Zero-shot COT Prompting

Image Source: Kojima et al. (2022) (opens in a new tab)

One recent idea that came out more recently is the idea of zero-shot CoT (opens in a new tab) (Kojima et al. 2022) that essentially involves adding "Let's think step by step" to the original prompt. Let's try a simple problem and see how the model performs:

The answer is incorrect! Now Let's try with the special prompt.

I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with? Let's think step by step.

First, you started with 10 apples. You gave away 2 apples to the neighbor and 2 to the repairman, so you had 6 apples left. Then you bought 5 more apples, so now you had 11 apples. Finally, you ate 1 apple, so you would remain with 10 apples.

It's impressive that this simple prompt is effective at this task. This is particularly useful where you don't have too many examples to use in the prompt.

Automatic Chain-of-Thought (Auto-CoT)

When applying chain-of-thought prompting with demonstrations, the process involves hand-crafting effective and diverse examples. This manual effort could lead to suboptimal solutions. Zhang et al. (2022) (opens in a new tab) propose an approach to eliminate manual efforts by leveraging LLMs with "Let's think step by step" prompt to generate reasoning chains for demonstrations one by one. This automatic process can still end up with mistakes in generated chains. To mitigate the effects of the mistakes, the diversity of demonstrations matter. This works proposes Auto-CoT, which samples questions with diversity and generates reasoning chains to construct the demonstrations.

Auto-CoT consists of two main stages:

Stage 1): question clustering: partition questions of a given dataset into a few clusters

Stage 2): demonstration sampling: select a representative question from each cluster and generate its reasoning chain using Zero-Shot-CoT with simple heuristics

The simple heuristics could be length of questions (e.g., 60 tokens) and number of steps in rationale (e.g., 5 reasoning steps). This encourages the model to use simple and accurate demonstrations.

The process is illustrated below:

Image Source: Zhang et al. (2022) (opens in a new tab)

Code for Auto-CoT is available here (opens in a new tab) .

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Chain-of-Thought Prompting | Prompt Engineering Guide

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A Comprehensive Overview of Prompt Engineering

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These refer to how many examples you provide in a prompt to help the AI understand and perform a task.

Zero-Shot: No examples are given; you just describe the task directly. This is ideal for simple, straightforward queries where the AI can infer what to do based on its training.

Why use it: It's efficient and quick for basic tasks, avoiding unnecessary clutter in the prompt.

Example: "Translate 'Hello, how are you?' to French." The AI directly outputs: "Bonjour, comment ça va ?"

Few-Shot: You provide a small number of examples (typically 2-3) to demonstrate the pattern or format. This is better for more complex or nuanced tasks where the AI needs context to "learn" on the fly.

Why use it: LLMs can generalize from examples, improving accuracy for tasks like classification, creative writing, or specific formatting.

Example: For sentiment analysis:

Prompt: "Classify the sentiment as positive, negative, or neutral.

Example 1: 'I love this movie!' → Positive Example 2: 'It was okay, nothing special.' → Neutral

Example 3: 'What a waste of time.' → Negative

Now classify: 'This book changed my life for the better.'"

AI output: Positive.

In general, start with zero-shot for simplicity, and switch to few-shot if the results aren't satisfactory. There's also "one-shot" (just one example) as a middle ground.

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Few-Shot Prompting | Prompt Engineering Guide

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A Comprehensive Overview of Prompt Engineering

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This is a higher-level technique where you prompt the AI to create, refine, or optimize prompts for a given task. Essentially, you're using the AI to help design better interactions with itself (or other AIs). It's like outsourcing prompt engineering to the model.

Why it works: Prompting can be an art, and meta-prompting leverages the AI's knowledge of language and tasks to generate more effective prompts, saving you time and iteration.

Example: Suppose you want to generate story ideas. Instead of crafting a detailed prompt yourself, you could say: "Create a better prompt for generating creative short story ideas about space exploration."

The AI might respond with: "Generate 5 original short story ideas about space exploration, each including a unique twist, conflict, and resolution. Make them engaging for young adults and limit each to 100 words."

You can then use that improved prompt in a follow-up query. This is especially useful for iterative tasks or when you're unsure how to phrase something optimally.

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A Comprehensive Overview of Prompt Engineering

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This method involves generating multiple responses to the same prompt (often using slight variations or repeated runs) and then selecting the most consistent or best one. It's like running an "election" among answers to find the majority vote.

Why it works: LLMs can be stochastic (random in outputs), so one response might be flawed, but aggregating several increases reliability. It's particularly helpful for ambiguous or reasoning-heavy tasks.

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A Comprehensive Overview of Prompt Engineering

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Don’t treat generative AI like a search engine where you enter a single query and expect a final answer. Instead, think of it as a dialogue with a knowledgeable assistant. AI models remember the context of your current session, so you can use follow-up prompts to refine, correct, or expand on a previous response. Phrases like, "That's a good start, but make it more formal," or, "Expand on your second point," will lead to more nuanced and tailored results.

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Your first prompt is rarely your last. Getting the best output is an iterative cycle of prompting, reviewing the response, and then refining your next prompt based on what you received. Don’t be discouraged if the initial result isn't perfect. Use it as feedback. Add more detail, provide examples of the desired output, specify a different tone or format, and try again. This gradual refinement is the key to mastering prompt engineering.

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When first learning to prompt, work with a subject where you have deep expertise. This is the fastest way to understand an AI model’s capabilities and limitations. Because you know the material, you can instantly recognize inaccuracies, subtle biases, or hallucinations. This practice builds essential critical evaluation skills and helps you learn how to phrase prompts to get more reliable results, even in subjects you know less about.

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To check for accuracy and logical consistency, ask the AI to explain its reasoning. Prompts like, "Explain your reasoning step-by-step," or, "What sources did you use to come to that conclusion?" force the model to show its work. This can reveal if its logic is sound or if it simply patched together plausible-sounding text. It’s a powerful technique for quality control that also pushes you to think more critically about the information you receive.

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No two AI models are identical. They have different training data, architectures, and strengths. Running the same prompt on different platforms (like Gemini, ChatGPT, Claude, etc.) will often yield surprisingly varied results. This comparative approach is excellent for understanding which tool excels at certain tasks—be it creative writing, factual synthesis, or code generation—and helps you select the best AI for your specific needs.

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Don’t exclusively use only one particular AI model!

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Don’t exclusively use only one particular AI model!

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Each AI model has its own training data, design philosophy, and limitations. Relying exclusively on one system can narrow your perspective and introduce blind spots or biases. By incorporating multiple AI tools into your workflow, you gain access to a broader range of insights, more diverse outputs, and a built‑in safeguard against over‑fitting your work to the quirks of a single platform. Diversifying your AI use increases accuracy, creativity, and overall reliability.

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= Task + Context + Examples + Persona + Format + Tone

Not every step is needed for all prompts, but, all together, they provide a handy checklist to optimize your outputs for your needs.​

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This is the foundation of your prompt: clearly state what you want the AI to do. Without it, you’ll get vague or irrelevant results.

Example: “Write a handout that helps students prepare for their final exam in Principles of Marketing.”

👉 Think of “Task” as the verb — create, explain, summarize, analyze, recommend, compare, design, simulate, etc.

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Context sets the stage by adding background details. This helps the AI tailor its response to your situation instead of giving you something generic.

Example: “This handout is for first-year students at a liberal arts college. The exam is cumulative, and many students are facing finals week for the first time.”

👉 Context answers: who is this for, where is it used, and why does it matter?

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Examples show the AI what “good” looks like. Instead of just telling it what to do, you can show the kind of answer you’d like.

Example: “Structure each tip like this: first a short piece of advice, then a quick explanation of why it matters. For instance, one tip might start with ‘Make a plan you can stick to,’ followed by a sentence explaining how breaking topics into smaller chunks helps.”

👉 This way, the AI understands the format and style you want.

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Persona sets the “voice” or role the AI should take on. This influences tone, authority, and perspective.

Example: “You are as a caring, supportive human who understands student stress and wants them to succeed academically without compromising their mental health.”

👉 Persona helps the AI sound less like a generic internet article and more like the trusted guide your students need.

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Format controls the shape of the output, ensuring it’s presented in a way you can actually use.

Example: “Present the handout as a bulleted list of 7 practical study tips, each no more than two sentences.”

👉 Format stops the AI from giving you long walls of text when what you need is something concise and shareable.

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Tone makes the content feel right for your audience. The same information can land very differently depending on the voice.

Example: “Keep the tone encouraging, approachable, and practical — less like a textbook, more like advice from a mentor.”

👉 Tone is what makes your handout engaging instead of dry.

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“Write a handout that helps students prepare for their final exam in Principles of Marketing. This handout is for first-year students at a liberal arts college. The exam is cumulative, and many students are facing finals week for the first time. Structure each tip like this: first a short piece of advice, then a quick explanation of why it matters. For instance, one tip might start with ‘Make a plan you can stick to,’ followed by a sentence explaining how breaking topics into smaller chunks helps. You are as a caring, supportive human who understands student stress and wants them to succeed academically without compromising their mental health. Present the handout as a bulleted list of 7 practical study tips, each no more than two sentences. Keep the tone encouraging, approachable, and practical — less like a textbook, more like advice from a mentor.”

Output from ChatGPT 5:

Preparing for Your Principles of Marketing Final Exam

Make a daily study schedule. Breaking the material into smaller pieces keeps it from feeling overwhelming and helps your brain remember more over time.

Review concepts, not just definitions. Knowing what “market segmentation” means is good—but being able to explain why it matters is what earns points on the exam.

Practice with old quizzes, notes, and sample questions. Active recall helps you spot gaps in your knowledge and builds exam confidence.

Teach the material out loud. If you can explain the 4 Ps of marketing to a roommate (or even to your cat), you truly understand them.

Mix up your study methods. Switch between rewriting notes, making flashcards, and drawing diagrams—variety keeps your brain engaged.

Take purposeful breaks. Short walks, snacks, or stretching give your mind time to recharge so your study sessions stay productive.

Rest before the exam. A well-rested brain processes information faster and recalls it more clearly than one running on coffee alone.