AI Chat - Short Courses

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Instruction-tuned large language models (LLMs) are advanced AI systems trained to follow user instructions. ChatGPT, Claude, and Gemini are common examples.  They are versatile and can perform tasks such as answering questions, writing essays, solving math problems, or summarizing text.

These models are designed to:

1. Understand Instructions: Respond clearly and accurately to user-provided prompts.
2. Adapt: Generate responses based on the tone, style, or specifics of your request.
3. Perform Broad Tasks: Handle diverse domains like writing, coding, education, and more.

Basic Prompting:

1. Task: Ask for information.

• Prompt: “Explain photosynthesis in simple terms.”
• Model Response: Photosynthesis is the process by which plants use sunlight, carbon dioxide, and water to create food and release oxygen.

1. Task: Ask for step-by-step instructions.

• Prompt: “How do I bake a chocolate cake?”
• Model Response: [Provides a step-by-step recipe.]

Activity: Try writing 3-5 simple prompts for various tasks (e.g., asking for definitions, instructions, or lists).

The quality of the response depends on the clarity and specificity of your prompt. Ambiguous prompts may yield vague answers, while detailed prompts provide precise results.

Tips for Effective Prompts:

1. Be Specific: Clearly state what you want.
2. Provide Context: Include background or examples if needed.
3. Ask Directly: Avoid open-ended or overly vague questions.

1. Specific vs. Vague Prompt:

• Vague: “Tell me about AI.”
• Specific: “What are the main applications of AI in education?”

1. Adding Context:

• Without Context: “Explain gravity.”
• With Context: “Explain gravity as if teaching a 10-year-old.”

1. Requesting Output Style:

• Prompt: “Write a poem about winter in a cheerful tone.”
• Model Response: [Generates a cheerful winter poem.]

Activity: Rephrase three vague prompts into specific, detailed ones and compare the responses.

Instruction-tuned LLMs can assist with:

• Creative Writing: Stories, poems, scripts.
• Educational Content: Summaries, explanations, or lesson plans.
• Problem Solving: Math, logic, coding.
• Language Translation: Translating text between languages.

1. Summarization:

• Prompt: “Summarize the main points of a news article about climate change.”
• Model Response: [Generates a concise summary.]

1. Creative Writing:

• Prompt: “Write a short story about a robot learning to paint.”
• Model Response: [Creates a narrative.]

1. Problem Solving:

• Prompt: “Solve this math problem: What is 25% of 160?”
• Model Response: 40.

Activity: Try one prompt from each task category (creative, educational, problem-solving, translation) and analyze the responses.

Sometimes, the model’s response might not fully meet your expectations. Refining the prompt or asking follow-up questions can lead to better outcomes.

Techniques for Refinement:

1. Provide Examples: Include samples of desired outputs.
2. Specify Format: Request lists, paragraphs, or structured answers.
3. Follow Up: Adjust the prompt or ask clarifying questions.

1. Refinement Through Examples:

• Initial Prompt: “Describe a character.”
• Revised Prompt: “Describe a character in detail, including their appearance, personality, and background.”

1. Requesting Format:

• Prompt: “List five ways to improve productivity.”
• Model Response: [Generates a numbered list.]

1. Follow-Up Questions:

• Initial Prompt: “What is the best way to learn a new language?”
• Follow-Up: “Can you list apps or tools to help with this?”

Activity: Start with a basic prompt, refine it based on the response, and try adding follow-up prompts to dig deeper into the topic.

As tasks become more complex, prompt structuring becomes critical. Advanced prompts often involve:

• Chaining Instructions: Requesting multiple steps or elements in one prompt.
• Setting Context First: Framing the task with a preamble or background.
• Using Variables or Parameters: Giving conditions or constraints.

1. Chaining Instructions:

• Prompt: “Explain Newton’s laws of motion briefly, and then give an example of each.”
• Model Response: [Provides a summary and examples for all three laws.]

1. Setting Context:

• Prompt: “Imagine you are an environmental scientist. Explain the effects of deforestation in a way that high school students can understand.”
• Model Response: [Tailors the response to the audience and context.]

1. Using Constraints:

• Prompt: “List three ideas for a science fair project that require a budget under $20.”
• Model Response: [Generates budget-friendly ideas.]

Activity: Write 2-3 chained or constrained prompts and observe how the responses align with your instructions.

Sometimes the initial response might need adjustments. Iterative feedback helps you refine outputs step-by-step by:

• Asking Follow-Up Questions: Clarify or expand on specific parts.
• Requesting Revisions: Direct changes to the format or content.
• Exploring Alternatives: Ask for different versions of the same task.

1. Follow-Up Question:

• Initial Prompt: “Describe the benefits of remote work.”
• Follow-Up: “Can you add disadvantages for balance?”

1. Requesting Revisions:

• Initial Prompt: “Write a poem about autumn.”
• Follow-Up: “Can you rewrite it in a humorous tone?”

1. Exploring Alternatives:

• Prompt: “Suggest a title for a mystery novel.”
• Follow-Up: “Can you provide three more options?”

Activity: Start with a prompt, refine it through two iterations, and compare the initial and final responses.

Different tasks require specific prompt strategies to optimize results. These tasks include:

• Data Extraction: Pulling specific information from text.
• Comparative Analysis: Asking for comparisons between options.
• Role-Playing: Instructing the model to adopt a persona for tailored responses.

1. Data Extraction:

• Prompt: “Extract the key dates and events from this text: [insert text].”
• Model Response: [Lists the dates and corresponding events.]

1. Comparative Analysis:

• Prompt: “Compare the pros and cons of solar energy and wind energy.”
• Model Response: [Provides a clear side-by-side comparison.]

1. Role-Playing:

• Prompt: “You are a career coach. How would you advise someone transitioning from teaching to a corporate role?”
• Model Response: [Generates personalized advice in the voice of a coach.]

Activity: Try one prompt for each task type and analyze how effectively the model performs.

LLMs can be powerful tools for collaboration, acting as sounding boards or co-creators. This involves:

• Brainstorming Ideas: Generate a wide range of possibilities.
• Refining Solutions: Build on initial ideas to create polished results.
• Proposing Alternatives: Evaluate and refine multiple approaches.

1. Brainstorming:

• Prompt: “Give me 10 unique ideas for a community service project.”
• Model Response: [Generates a diverse set of ideas.]

1. Refining Solutions:

• Initial Prompt: “Suggest an idea for a small business.”
• Follow-Up: “What are the potential challenges of this idea, and how can they be addressed?”

1. Proposing Alternatives:

• Prompt: “Suggest different ways to teach fractions to elementary school students.”
• Follow-Up: “Can you suggest a more interactive approach?”

Activity: Use the model to brainstorm a topic of your choice, refine the ideas, and propose alternative solutions.

Instruction-tuned LLMs can handle extended conversations where previous inputs influence subsequent responses. This enables:

• Building Context: Carrying information from one prompt to the next.
• Dynamic Refinement: Adjusting the direction of a conversation based on evolving needs.
• Simulating Real-World Interactions: Role-playing or simulating scenarios across multiple turns.

1. Building Context:

• Prompt 1: “Explain the lifecycle of a butterfly.”
• Prompt 2: “Now, compare this to the lifecycle of a frog.”
• Model Response: [Draws on the butterfly explanation to create a comparison.]

1. Dynamic Refinement:

• Prompt 1: “Outline a marketing strategy for a tech startup.”
• Prompt 2: “Can you expand the strategy to include social media campaigns?”
• Model Response: [Builds on the initial strategy.]

1. Simulating Real-World Interactions:

• Prompt: “Pretend to be a job interviewer. Ask me three questions for a software engineering role.”
• Model Response: [Provides realistic interview questions.]

Activity: Create a multi-turn conversation, starting with a general topic and refining it step-by-step across three or more prompts.

Models can mimic styles or personas when explicitly directed. This allows for:

• Role-Based Interactions: Interacting as a professional, teacher, or fictional character.
• Stylistic Adaptation: Adopting tones like formal, casual, persuasive, or poetic.
• Specialized Perspectives: Writing from a historical, scientific, or creative viewpoint.

1. Role-Based Interaction:

• Prompt: “You are a financial advisor. What investment strategies would you recommend to someone with a low risk tolerance?”
• Model Response: [Generates a tailored financial plan.]

1. Stylistic Adaptation:

• Prompt: “Write a review of this product in a humorous tone.”
• Model Response: [Creates a lighthearted, funny review.]

1. Specialized Perspectives:

• Prompt: “Write a letter to a friend as if you were Albert Einstein, explaining relativity.”
• Model Response: [Emulates Einstein’s perspective and voice.]

Activity: Try role-playing or stylistic prompts, asking for responses in different tones or from specialized viewpoints.

Task chaining involves breaking down a complex goal into smaller, sequential tasks that the model can handle step-by-step. This is useful for:

• Research and Summarization: Collecting, organizing, and synthesizing information.
• Creative Processes: Developing ideas iteratively, from brainstorming to execution.
• Complex Problem-Solving: Tackling challenges that require logical sequencing.

1. Research Workflow:

• Prompt 1: “Find the main arguments for and against renewable energy adoption.”
• Prompt 2: “Summarize these arguments in bullet points.”
• Prompt 3: “Draft a balanced conclusion based on the summary.”

1. Creative Process:

• Prompt 1: “Generate 10 potential names for a fantasy novel.”
• Prompt 2: “Select the best three and suggest a plot summary for each.”

1. Problem-Solving:

• Prompt 1: “Plan a week-long itinerary for a family visiting Paris.”
• Prompt 2: “Add budget considerations for meals and transportation.”

Activity: Develop a task chain for a topic of your choice, starting with research, moving to synthesis, and ending with actionable insights.

Advanced users can guide the model to:

• Format Outputs Precisely: Request tables, outlines, or formatted text.
• Incorporate Constraints: Limit outputs by word count, style, or content focus.
• Generate High-Fidelity Outputs: Ensure accuracy and relevance through iterative refinement.

1. Precise Formatting:

• Prompt: “Create a pros and cons table for electric vehicles.”
• Model Response: [Generates a neatly formatted table.]

1. Incorporating Constraints:

• Prompt: “Write a 150-word summary of the French Revolution, focusing only on its causes.”
• Model Response: [Produces a concise, focused summary.]

1. High-Fidelity Outputs:

• Prompt: “List the steps to set up a small business, ensuring compliance with U.S. tax laws.”
• Follow-Up: “Can you elaborate on the tax compliance steps?”

Activity: Use constraints or formatting requirements in your prompts and compare the results with less specific versions.