Creating your AI Learning Dashboard is like drawing a simple treasure map that shows where you are, where you're going, and how you’ll get there.

You pick your goals, list the skills you want to learn, and mark small checkboxes so you always know what you’ve finished.

Think of it like a video game: your goals are “quests,” your skills are “levels,” and your checkpoints are “save points.”

This helps you learn faster because you can actually see your progress.

You can build this dashboard in Notion, Trello, or Google Sheets — whichever feels easiest. By the end, you’ll have a personal command center that tells you exactly what to do next.

INTERACTIVE EXERCISE: Detailed Instructions (Beginner-Friendly)

Objective:
Build a simple “AI Learning Dashboard” that organizes your goals → skills → weekly checkpoints.

Outcome:
One page/board/sheet that shows your learning plan visually.

WHAT You’re Creating

A tiny dashboard containing 3 sections:

1. Your AI goals (what you want to be able to do)
2. Your skill tracks (the 3–5 skills that help you reach those goals)
3. Your weekly checkpoints (the tiny steps that keep you on track)

WHY You’re Doing It

Because:

• ✔ AI learners who track their progress learn 2–3× faster
• ✔ Your brain loves visual markers
• ✔ You can “save your state” each week (just like a game)
• ✔ It becomes your AI co-pilot home base for Week 1 onward

HOW TO BUILD IT (Universal Beginner Steps)

These steps apply no matter which tool you choose.

STEP A: Create a new workspace

• Open Notion, Trello, or Google Sheets.
• Create a new page / new board / new sheet.
• Name it: AI Learning Dashboard – Week 1

STEP B: Add a simple 3-part layout

Write (or create columns/cards):

1. Goals
2. Skills
3. Weekly Checkpoints

STEP C: Fill in your base content

Use these starter templates:

1. Goals (choose 2–3 max)
Examples:

• “Write better prompts”
• “Use AI to speed up my work”
• “Start an AI-powered side hustle”

2. Skill Tracks (3–5 skills)
Examples:

• Prompting
• Research
• Automation
• Data literacy
• Creative generation
• AI tools setup

3. Weekly Checkpoints (small steps)
Examples:

• Watch 1 tutorial
• Practice 3 prompts
• Test 1 new AI tool
• Share 1 screenshot in community
• Complete 1 challenge task

STEP D: Add checkboxes or cards

This gives you an easy way to track progress.

Every tool supports checkboxes or “cards” with a status.

STEP E: Customize (optional but recommended)

• Add emojis
• Color code columns
• Add “Completed” section/folder
• Add dates or weekly labels
• Add a small “Notes” or “Wins” section

STEP F: Save + Share

You should screenshot or share a link in the “Share Your AI Dashboard Screenshot” thread.

TOOL-SPECIFIC VERSIONS

Below are three complete beginner walkthroughs for each platform.

OPTION 1 — NOTION (Simplest for beginners)

1. Create the page

• Open Notion → left sidebar → + New Page

• Name it AI Learning Dashboard – Week 1

2. Insert section headers

Copy & Paste:

3. Add toggles for each

Type “/toggle” and add:

• “Goal 1”

• “Goal 2”

• “Goal 3”

Repeat for Skill Tracks and Checkpoints.

4. Add checkboxes

Type “/to-do” under each section to add your tasks.

5. Add a “Completed” section

Copy & Paste:

6. Screenshot + Share

Take a screenshot → upload to the Week 1 thread.

🟩 OPTION 2 — TRELLO (Best for visual learners)

1. Create the board

• Go to Trello.com

• Click Create Board

• Name: AI Learning Dashboard – Week 1

2. Add three lists:

• Goals

• Skills

• Weekly Checkpoints

3. Add cards inside each list

Example cards:

• Goal: “Learn prompting”

• Skill: “Prompt patterns”

• Checkpoint: “Practice 3 prompts today”

4. Add labels

Click a card → Add Labels
Use colors for categories (e.g., green = done).

5. Create a “Completed” list

Drag finished cards here.

🟨 OPTION 3 — GOOGLE SHEETS (Best for analytical people)

1. Create the sheet

• Go to sheets.new

• Title the doc: AI Learning Dashboard – Week 1

2. Set up columns:

Row 1:

3. Add rows for each category:

4. Add checkboxes

• Select column C

• Insert → Checkbox

5. Color categories

Use Format → Fill color.

6. Optional: add a progress bar

Use:

7. Screenshot + post

🚀 ADVANCED VERSION (For AI Income Lab power users)

This is the “Level 2” upgrade path.

1. Add a “Learning Engine Loop” section

Includes:

• Input → Something you learned

• Process → How you applied it

• Output → What improved

• Feedback → What you’ll try next

• Next Step → Action

2. Add AI-generated weekly suggestions

Example fields:

• “AI Recommendation for this week”

• “Prompt I’m practicing”

• “Tool I’m testing”

• “Skill I’m leveling up”

(Optional: You can paste a GPT-generated “weekly sprint plan” right into your dashboard.)

3. Add automation (optional)

• Notion: Add formulas for % completed

• Trello: Add automation to move completed cards

• Google Sheets: Add conditional formatting for deadlines

4. Add a “Wins Log”

A single section for all micro-wins:

• First good prompt

• First automation

• First AI-generated asset

• First time something saved you time or money

This builds momentum.

Imagine your AI journey is like walking a hiking trail. You don’t just aim for the mountain peak; you look for tiny trail markers that keep you on track.

Weekly checkpoints are those markers. They make sure you’re improving, not drifting or getting stuck. Each week, you stop, look around, and ask AI to show you what’s working and what you should adjust.

It’s like having a friendly guide who checks your map with you. These small “check-ins” build momentum and help you learn faster without overwhelm.

WHAT THIS STEP IS

Weekly checkpoints help you make steady progress without guessing.

You build a simple habit: review what you learned, reflect on progress, and refine your plan using AI as your thinking partner.

They connect directly to your AI Learning Roadmap and Dashboard from Step 2, making your personal system “alive” — something that updates as you learn.

WHY CHECKPOINTS MATTER

• They prevent burnout by breaking big goals into bite-size wins.
• They help you track actual progress, not just “hoping you learned something.”
• They turn AI into a metacognitive assistant, meaning it helps you think about your thinking.
• They help you notice patterns (“I learn better visually,” “I get stuck on jargon,” “I skip practice tasks”).
• They make your learning path adjust to real life, not stay stuck as a plan you never revisit.

HOW TO DO IT (Beginner-Friendly Instructions)

1. Create a simple recurring weekly section in your dashboard

In Notion, Trello, or Google Sheets, create one repeating area titled:

“Weekly Checkpoint – Week X”.

You only need three fields:

1. What did I work on?
2. What was confusing or challenging?
3. What should I improve or try next week?

That’s it — three questions.

2. Copy a Weekly Checkpoint Template

Use this reusable template (you can paste into any platform):

WEEKLY CHECKPOINT TEMPLATE

Week #:

Dates:

1. What I learned this week:
2. What I tried (tools, prompts, assignments):
3. What I struggled with or didn’t understand fully:
4. What AI says I should improve next week: (paste AI’s feedback)
5. What I commit to doing next week:

3. Use “Self-Refine Prompting” during your checkpoint

Ask AI to help you evaluate and improve your learning, just like you would improve a draft.

Paste your weekly reflections into ChatGPT and use this prompt:

SELF-REFINE CHECKPOINT PROMPT

“Here’s my weekly checkpoint.
Review my progress, identify what I’m missing, and suggest the next best steps.
Then critique your own suggestions for clarity and rewrite them to be even clearer.”

This does three things instantly:

1. AI analyzes your progress (so you don’t have to guess).
2. AI critiques itself, which models metacognitive skill for you.
3. AI rewrites the advice more clearly, giving you an actionable next-week plan.

4. Add a “Next Week Plan” to your Dashboard

Take the final, refined advice from AI and paste it into:
“Next Week – Focus Items”

This keeps your Dashboard always updated and relevant.

5. Repeat weekly (the checkpoint becomes your momentum engine)

The system is intentionally simple so you can sustain it:

• 15 minutes once a week
• paste notes → get AI feedback → update your Dashboard

Small loops create big gains.

OPTIONAL: Want it even easier? Use the Auto-Generated Weekly Prompt Block

Put this mini-prompt block at the bottom of every checkpoint:

“Summarize my week in 3 bullet points, tell me one thing I should celebrate, one thing I should fix, and one thing I should learn next.”

This version is perfect for beginners who want low-friction progress tracking.

Imagine your AI learning goals are puzzle pieces. A mindmap helps you see how all the pieces connect, so you don’t feel lost. Instead of keeping everything in your head, you draw a simple “map” showing the skills you want to learn, how they relate to each other, and where they lead. It’s like building a trail map for your own learning journey.

1. What This Step Is About

This step teaches you how to take all the skills, tools, and checkpoints from your AI Learning Roadmap and turn them into a visual map.

A mindmap shows:

• What skills belong together
• Which skills depend on others
• What you should learn first
• Where your creativity can branch out
• How your goals connect across tools, platforms, and use cases

You will dramatically accelerate in Week 1 once you can see how everything is connected.

2. Why Visualizing Connections Matters

A mindmap gives you:

• Clarity: No more guessing what to learn next
• Confidence: You see progress forming a structure
• Momentum: Small wins become visible nodes
• Creativity: You notice new pathways you didn’t see before
• Better Prompts: Because you can articulate your skill gaps clearly

The brain loves visual patterns. This step taps into that.

3. The Goal of This Exercise

By the end of Step 4, you will have:

• A simple, clean mindmap showing your chosen AI skills and how they relate
• A visual “hub” you can return to weekly
• A tool you will refine in later weeks (as part of your AI Stack, Prompt Library, and Project Planning)
• A deeper understanding of your own learning journey

This visual becomes one of your three foundational Week 1 deliverables.

4. What You’ll Create (Beginner-Friendly)

• ✔ A central bubble with your Week 1 goal
• ✔ Branches for the skill tracks you selected in Step 2
• ✔ Mini-branches for tools, concepts, and checkpoints
• ✔ Optional: Expressions of interest (projects you may build later)

Think of it like drawing the solar system—with your learning goal in the center, and each skill orbiting around it.

5. Step-by-Step: How to Make Your Mindmap (No Experience Required)

Use whichever platform feels easiest:

• Google Drawings (fastest beginner option)
• Miro or Figma (for visual thinkers)
• MindMeister (classic mindmapping tool)
• Jamboard alternative like FigJam (excellent for collaboration)

Step A: Start With the Center

Write your main goal in the middle: “My Week 1 AI Learning Goal”

Step B: Add Your Skill Tracks

From Step 2 (Mini Dashboard), add branches such as:

• Prompting
• AI Tools
• Automation
• Writing/Content
• Data & Analysis
• Creative/Visual

Step C: Add Sub-Skills From Your Checkpoints

Each skill gets 2–4 sub-branches.
Example for Prompting:

• Self-Refine prompts
• Multi-step prompts
• Role-based prompting
• Thinking frameworks

Step D: Add “Connections”

This is the key metacognition moment:
Draw lines or arrows showing how skills support each other.

Ex:
Prompting → Research → Automation
Creative Tools → Visual Communication → Marketing Projects

Step E: Label Gaps or Questions

These become future checkpoints.
Examples:

• “How do I use Notion AI for content?”
• “What’s the simplest way to automate weekly tasks?”
• “What tools help me edit my prompts?”

Step F: Final Self-Refine Pass

Ask the AI to help make your map cleaner:
Prompt:
“Here’s my mindmap. Critique it for clarity, then help me reorganize it to make it easier to follow.”

This builds metacognitive thinking, because AI helps you see patterns you missed.

6. Extra Tip: Use Color Coding

• Blue = Skills
• Green = Tools
• Yellow = Checkpoints
• Pink = Questions/Gaps
• Purple = Project Ideas

This makes your learning visually intuitive.

7. What to Expect After Completing This Step

You’ll immediately feel:

• More organized
• More aware of how AI fits into your real work
• More motivated
• More capable of planning projects
• More comfortable using AI for thinking, not just answers

This mindmap becomes your visual guide for the remaining Week 1 activities.

Main Explanation (Elaborate but Simplified)

A learning loop is a simple cycle that helps you learn faster. It's the engine that makes your AI practice actually work.

Each round teaches you something new.

It’s like cooking the same recipe each week and each time you adjust the spice, heat, or timing until it tastes perfect.

AI helps you improve each round because it shows you mistakes and gives new ideas. The goal is not perfection, it’s steady improvement.

You try something, check what worked, fix what didn’t, and try again.

Instead of doing something once and hoping you understand it, you repeat a very small cycle:

1. Do the task (write a prompt, make a dashboard, try a tool).
2. Reflect (What went well? What confused you?).
3. Ask AI to refine or correct your attempt using Self-Refine Prompting.
4. Try again with one small improvement.
5. Save the results inside your dashboard or notes. Each loop is tiny, repeatable, and compounds quickly. You’re not memorizing—you’re iterating.

Self-Refine Prompting Pattern for Learning Loops

This is the simple pattern you’ll use each time:

1. Write a summary

“Here’s what I did and what I was trying to learn…”

2. Ask AI to critique it

“Show me where I can be clearer or more accurate…”

3. Ask AI to rewrite it more clearly

“Rewrite it in a simpler and clearer way that improves my attempt…”

That’s it. You’ve now turned a single attempt into a loop that upgrades your skills every time.

The Beginner-Friendly Walkthrough (Copy-Paste)

STEP A: Start with a tiny task

Pick something small:

• Write a short prompt
• Outline 3 bullets
• Build 1 card in your dashboard
• Try one setting in a tool

Tiny tasks make looping easy.

STEP B: Capture your attempt

Write a quick note:

“This is what I tried and why.”

Don’t worry about writing it well. You’re about to use the loop to improve it.

STEP C: Ask the AI to refine your attempt (the Loop Moment)

This is the core prompt:

Self-Refine Loop: “Here is my attempt. Summarize it, critique it for clarity, and rewrite it to make it simpler and more effective.”

This forces the AI to do three things:

1. Understand your thinking
2. Identify improvements
3. Rewrite it into a stronger version

Your skill improves every time you run these 3 steps.

STEP D: Apply one improvement

Choose ONE of the changes the AI suggests and test it. Small improvements stack fast.

STEP E: Save your new version

Put it into your dashboard under:

• Skill Track: what this relates to
• Checkpoint: today’s improvement
• Notes: what you learned

This creates your Learning History, which is crucial in Week 2 and Week 3.

STEP F: Repeat the loop next time you try the task

Your goal isn’t to get it “right.” Your goal is to get it slightly better each round.

What a Full Learning Loop Looks Like (Example)

Task: Write a better research prompt.

1. Attempt:

“My prompt didn’t give me the sources I wanted.”

2. Self-Refine Prompt:

“Summarize my attempt, critique it, and rewrite it clearer.”

3. AI Output:

• Summary
• Critique
• Better rewritten prompt

4. Improvement Applied:

You use the new prompt.

5. Saved in Dashboard:

“Improved my research prompt using the Self-Refine process.”

This becomes one “loop.”

Run 10 loops → feel 10× more confident.

Run 30 loops → you’ve built an actual skill.

Create a learning loop (journal → reflect → prompt AI → improve → share).

AI gives better answers when you tell it who to act as, what to do, and why you need it.

It works like giving instructions to a chef: name the chef, name the dish, and name the purpose.

Clear instructions help AI give clear results.

After it responds, ask it to improve its own answer. This makes your results better each time. It’s an easy, reliable way to start prompt engineering.

1. What This Step Teaches

Before you build advanced prompts, you only need one simple formula:

1. Role + Action + Goal
2. This is the “starter kit” of prompt engineering.
3. It tells the AI who to be, how to act, and what outcome you want.

Think of it like giving directions to a friend:

1. “Be the driver” (Role)
2. “Take us to the café” (Action)
3. “Because I need to pick up my order” (Goal)

Same idea—just applied to AI.

2. Why This Formula Works

This formula is the fastest way to sharpen your prompts because it:

• Gives the AI context (so it knows how to respond)
• Gives a task (so it knows what to produce)
• Gives a target (so results match your needs)

Without these three, prompts become vague.
With them, prompts become powerful—even if simple.

3. How to Use It (Super Simple Steps)

Step A: Start with the ROLE

Tell the AI who it should “pretend” to be so it uses the right expertise.

Examples:

• “You are a cooking tutor…”
• “You are a branding expert…”
• “You are a youth educator…”
• “You are my writing partner…”

Step B: Give the ACTION

Tell it exactly what it should produce.

Examples:

• “...explain this concept…”
• “...rewrite this paragraph…”
• “...analyze this idea…”
• “...draft a list of steps…”

Step C: Tell it the GOAL

Explain what the output is for, or what problem it should solve.

Examples:

• “...so I can teach this to beginners.”
• “...so I can include this in my blog.”
• “...so it becomes easier to understand.”

This final piece is what turns a generic answer into a custom one.

4. Example Prompt (Beginner Friendly)

“You are an AI tutor. Explain diffusion models in simple language so anyone new to AI can easily understand.”

Breakdown:

• Role: AI tutor
• Action: Explain diffusion models
• Goal: Make it accessible for beginners

5. Self-Refine Prompting (Build Metacognition)

After the AI answers, use this sentence:

“Improve your answer. Critique it for clarity, then rewrite it to be clearer.”

This improves:

• explanation quality
• structure
• readability

6. Mini Exercise (For Learners)

Try this quick 30-second activity:

1. Choose a task you want AI to help with.
2. Write a prompt using the formula: Role + Action + Goal
3. Send it to ChatGPT.
4. Then ask it to refine itself: “Critique your answer for clarity and rewrite it to be clearer.”

You'll immediately see how the quality improves when you use this formula.

What This Step Is About

This step introduces 20 prompting techniques that will help you get better, clearer, and more useful results from ChatGPT (or any AI tool).

Think of these techniques as the “power tools” of prompting. They're simple ideas that make everything easier once you know them.

You don’t need to memorize all 20 today.

Just explore, experiment, and find a few that instantly make sense.

The goal is to help you become more confident, curious, and intentional with how you communicate with AI.

Why This Matters

Most people use AI like a search bar, just typing whatever comes to mind.

But AI responds best when you give it structure, direction, and a clear purpose.

Learning these 20 techniques helps you:

• Get faster, more accurate results
• Save time when researching, writing, or creating
• Build stronger problem-solving habits
• Understand how AI “thinks” and why clarity matters
• Develop metacognitive skills (thinking about how they’re thinking)

When you understand how to prompt well, everything else in the AI Income Lab becomes easier.

Breakdown: What You’ll Learn in This Step

1. The Core Idea Behind Prompt Engineering

Prompting is not about being clever!
It's about being clear, specific, and purpose-driven.

A good prompt usually answers three questions:

1. What role do you want the AI to take?
2. What action do you want it to perform?
3. What goal or output should it produce?

This is the core formula: Role + Action + Goal.

The 20 techniques simply give you variations of that formula.

2. What “Self-Refine Prompting” Means

One of the main techniques you’ll practice here is: Ask the AI to improve its own answer.

It works like this:

• Step 1: Ask for the output.
• Step 2: Ask the AI to critique that output (clarity, flow, accuracy, structure).
• Step 3: Ask it to rewrite based on its own critique.

This teaches you to:

• slow down
• review your thinking
• upgrade drafts
• treat the process as a collaboration
• builds metacognitive thinking

3. Overview of the 20 Prompting Techniques

• Role Prompts: “Act as a teacher, designer, coder…”
• Process Prompts: “Walk me through step-by-step…”
• Checklist Prompts: “Evaluate this using these criteria…”
• Rewrite Prompts: “Rewrite this shorter, clearer, friendlier…”
• Compare & Contrast Prompts: “Show me 3 options and explain the differences…”
• Scenario Prompts: “Imagine you are advising a beginner in this situation…”
• Self-Refine Prompts: “Critique your answer, then improve it.”
• Socratic Prompts: “Ask me thoughtful questions before responding.”
• Template Prompts: “Use this structure: A, B, C…”
• Reverse Prompts: “Tell me what information you need from me…”

These techniques help you explore different angles, clarify your thinking, and get better-quality outputs.

4. How You Should Use This Step

• Pick 2–3 techniques that feel most natural
• Test them on a real task (writing, studying, researching, brainstorming)
• Notice how each technique changes the result
• Mix techniques together (ex: Role Prompt + Checklist + Self-Refine)

This turns prompting into a skill, not guesswork.

1. What Are Agentic Workflows?

Think of an agentic workflow as giving your AI a small job, clear rules, and a goal, and then letting it run the steps for you.

1. Agentic workflows are when AI follows a set of steps automatically, like a mini-assistant.
2. Instead of doing one prompt at a time, the AI carries out a sequence: think → act → check → improve.
3. This lets it complete small projects for you, not just answer questions.
4. You give it the goal and the rules, and it handles the busywork.
5. It's like telling a helper: “Do this, and if it’s wrong, fix it before giving it back.”
6. This makes your work faster, cleaner, and more consistent.

Analogy:

If normal prompting is asking a baker to make one cookie, an agentic workflow is giving them a full recipe and letting them bake the whole batch while checking their own mistakes.

2. Why This Matters for Beginners

Here’s the big idea:
Most people use AI like Google. Only one question at a time.
Agentic workflows turn that into a repeatable system that works while you focus on creative decisions.

They help you:

• Reduce mistakes
• Automate repetitive steps
• Get consistent results
• Build better habits for learning
  Save time while exploring new ideas

This is the bridge between basic prompting and advanced AI skills.

3. The Core Formula: Think → Act → Reflect → Improve

This is the “brain loop” of an agentic workflow.

✔️ THINK
AI explains how it will approach the task.
“Here’s the plan.”

✔️ ACT
AI produces the output.
“Here’s my first attempt.”

✔️ REFLECT
AI critiques its own work.
“Here’s what I can improve.”

✔️ IMPROVE
AI rewrites, rebuilds, or refines.
“Here’s the improved version.”

This is literally a built-in “self-refine” cycle... which is one of your 20 Prompting Techniques.

4. A Beginner-Friendly Template You Can Copy

Paste this into ChatGPT (or any model):

You are now running a simple agentic workflow.

GOAL:
[Insert what you want to achieve]

TASK:
Follow this cycle:
1. THINK — Plan the steps.
2. ACT — Produce the first version.
3. REFLECT — Critique the results for clarity, accuracy, and usefulness.
4. IMPROVE — Provide an improved version.

Rules:
- Keep all reflections visible.
- Explain your reasoning simply.
- Stop only when I say “complete.”

Begin with: THINK.

This teaches the AI how to run a mini-agent loop for you.

5. Easy Real-World Examples Students Can Try

Example A: Turn Notes Into a Study Guide

Goal: “Use an agentic workflow to make a clean study guide from my messy notes.”

Outcome: AI plans → formats notes → checks clarity → refines → completes.

Example B: Brainstorm Business or Project Ideas

Goal: “Use the think → act → reflect → improve cycle to generate 5 project ideas and polish the best one.”

Outcome: AI cycles through your ideas and improves them automatically.

Example C: Write, Edit, and Improve Content

Goal: “Write a 2-paragraph summary, critique your own clarity, and rewrite it.”

Outcome: A perfect demonstration of the self-refine technique in action.

6. How to Know You’re Doing It Right

You’ll notice you’re in an “agentic” flow if:

1. The AI explains its approach before acting
2. It critiques itself without being asked
3. Each version improves noticeably
4. You spend less time fixing things
5. You see the AI behaving more like a helper than a search bar

You're not trying to build advanced agents.
You’re learning how to think with the AI, not just use the AI.

7. A Simple Challenge for Workshop Participants

Add this to your AI Learning Dashboard under Week 1 → Experiments:

Challenge: Run one agentic workflow today using the provided template.

Choose one:

1. Create a polished summary
2. Brainstorm 10 ideas → refine the best one
3. Convert messy thoughts into a plan
4. Rewrite a page of notes
5. Improve an email, pitch, or caption

Afterward, record:

• What did it improve?
• What did it miss?
• What would you try next time?

This reflection reinforces metacognitive learning.

8. The Big Takeaway

Agentic workflows = Simple loops that help AI think, check, and improve, so you produce better work with less effort.

You don’t need coding.
You don’t need automation tools.
You only need a good prompt and a clear goal.

Mastering this early gives you a superpower for Week 2, Week 3, and beyond.

How to Save Each Successful Prompt

Think of prompts like recipes. If it works, keep it.

Do this:

1. When a prompt gives a great result → save it.
2. Ask the AI to self-refine it first (improve, critique, rewrite).
3. Store the final version in Notion, Google Docs, Trello, or your dashboard.
4. Add a short note: “This prompt is for ___. Use it when ___.”
5. Review your saved prompts weekly and improve them over time.

Goal: Build your own “Prompt Stack” that gets better every week.

Learn How to Tag Prompts by Purpose

(Why it matters & how to do it in the simplest, most repeatable way)

Tagging your prompts by purpose is how you turn a messy pile of ideas into a usable, searchable library.
Think of this as giving every prompt a “job title” so you instantly know when to use it. When your prompts are tagged, you no longer have to hunt through long chats or guess which one worked — the tag tells you exactly what it’s good for.

Tagging also trains your brain (and AI) to think metacognitively: What kind of thinking am I asking the AI to do?

This is the foundation of becoming a strategic AI user.

1. What “Tagging by Purpose” Means (Plain Language)

Instead of saving random prompts, you save them with a clear intention label:

• GENERATION: create something new (ideas, drafts, lists, outlines).
• TRANSFORMATION: rewrite, improve, shorten, restructure, simplify.
• ANALYSIS: break things down, explain patterns, compare, critique.
• DECISION: choose between options, rank, recommend, evaluate.
• PLANNING: create steps, timelines, roadmaps, workflows.
• SELF-REFINE: ask AI to critique its own output and improve it.

These tags become your “prompt toolkit” for every type of task.

2. Why Tagging Prompts Makes Your Life Easier

Tagging:

• Makes your prompt library searchable.
• Helps you instantly choose the right tool for the job.
• Reduces guesswork so you move faster and write better prompts.
• Builds “muscle memory” for the logic behind every prompt.
• Helps your students, colleagues, or team navigate prompts without confusion.

This is how beginners become intermediate fast.

3. How to Tag a Prompt in Under 10 Seconds

Take any prompt you like and add a short label at the top:

[PURPOSE: GENERATION]
[PURPOSE: ANALYSIS]
[PURPOSE: SELF-REFINE]

Example:
[PURPOSE: TRANSFORMATION]
“Rewrite this paragraph to be clearer, more concise, and more educational.”

That’s it. Now the next time you come back to it, you instantly know why it exists.

4. How to Use Self-Refine Prompting with Tags

Every tag can be paired with a self-refinement step:

Example:
[PURPOSE: GENERATION + SELF-REFINE]
“Create a list of 10 ideas. Then critique the list for clarity and usefulness.
Rewrite the list to improve it.”

By pairing tags with self-refinement:

• AI becomes more accurate.
• Your output improves automatically.
• You build metacognitive awareness of how prompts work.

This is where students begin to learn how to think with AI instead of just using it.

5. The Simplified Workflow (Copy This Into Your Dashboard)

STEP A: Write the prompt.
STEP B: Add a purpose tag.
STEP C: Add a self-refine instruction (optional but highly recommended).
STEP D: Save it to your prompt library under the correct category.

Example library categories:

1. Brainstorming Prompts (Generation)
2. Rewriting Prompts (Transformation)
3. Critique & Compare Prompts (Analysis)
4. Planning & Roadmaps (Planning)
5. Decision-Making Prompts (Decision)
6. Self-Refine Templates

6. ELI5 Analogy (Simple Version for Beginners)

Tagging your prompts is like labeling drawers in a toolbox.

If you dump all the tools into one drawer, you waste time digging.

But if one drawer says “Screwdrivers,” another says “Wrenches,” and another says “Measuring tools,” you always know exactly where to reach, and you get the job done faster.

That’s what prompt tagging does for your AI workflow.

7. A Ready-to-Use Template (Paste This Anywhere)

[PURPOSE: ______ ]
Task: ________________________________
Context: ______________________________
Output Style: __________________________
Self-Refine: “Critique your answer for clarity and usefulness. Then rewrite to improve it.”

Use this template for every prompt you want to save.

To use AI confidently in your daily life and work, it helps to understand five core “pillars.”

Think of them like the five sides of a toolkit you carry everywhere:

1. one side helps you find good information,
2. one helps you communicate clearly,
3. one helps you create things,
4. one keeps you safe, and
5. one teaches you how to solve problems smarter.

These pillars come from global digital-literacy frameworks and translate beautifully to modern AI use.

Each pillar below includes a quick explanation plus a simple “Self-Refine Prompt” you can try to deepen your skill.

Use this to understand the foundations of modern digital citizenship in an AI-saturated world.
You’ll learn the five essential pillars:

1. Awareness – what AI is (and isn’t).
2. Ethics – fairness, accountability, transparency.
3. Safety – privacy, data handling, boundaries.
4. Creativity – using AI as a thinking and creation partner.
5. Agency – knowing when you should stay in control.

🟦 Pillar 1 — Information & Data Literacy

What it means:
Understanding how to find information, evaluate it, compare sources, and check whether AI-generated responses are accurate or trustworthy.

Why it matters:
AI can sound confident even when it's wrong. Your job is to guide it, verify facts, and think critically.

Try a Self-Refine Prompt:
“Give me three facts about this topic. Then critique your own answer for accuracy and rewrite it with improved clarity.”

🟩 Pillar 2 — Communication & Collaboration

What it means:
Using AI to improve communication—writing emails, explaining ideas, translating messages, or organizing projects.

Why it matters:
Workplaces are now hybrid, global, and fast-moving. AI helps you express clearer ideas and collaborate more effectively.

Try a Self-Refine Prompt:
“Write this message politely and clearly. Now critique your tone and rewrite it with better professionalism.”

🟧 Pillar 3 — Digital Content Creation

What it means:
Creating written content, images, videos, presentations, or plans with the help of AI tools.

Why it matters:
Creative work used to take hours. Now you can draft, iterate, and refine in minutes, as long as you still direct the vision.

Try a Self-Refine Prompt:
“Make a first draft of this idea. Now evaluate your structure and rewrite it with a stronger beginning, middle, and end.”

🟥 Pillar 4 — Safety & Well-Being

What it means:
Understanding privacy, data ethics, safe AI use, emotional well-being, and how to avoid harmful or misleading AI outputs.

Why it matters:
AI is powerful, but without boundaries, because it can overwhelm, mislead, or expose personal information.

Try a Self-Refine Prompt:
“Explain the risks of sharing this type of data with AI. Now review your explanation and rewrite it to be safer and more accurate.”

🟪 Pillar 5 — Problem-Solving & Critical Thinking

What it means:
Using AI as a thinking partner to compare options, analyze scenarios, design workflows, and explore solutions.

Why it matters:
AI doesn’t replace your judgment. It amplifies it.
You ask better questions → AI gives better answers → you refine → results improve.

Try a Self-Refine Prompt:
“Propose two solutions. Critique both for weaknesses, then generate an improved final recommendation.”

Get a snapshot of your current level of understanding.

This quiz helps you see what you already know about using AI safely and smartly. It checks things like: “Can you spot bias?”, “Do you know what AI can’t do?”, and “Can you tell when an answer needs to be double-checked?”

There are no trick questions. It’s just a map of your current skills. After the quiz, you’ll see your strong areas and the places you can grow next.

You’ll use that map to guide the rest of Week 1. Then you’ll try a small reflection activity where the AI improves its own explanation.

This teaches you how to think with AI, not just about AI. Your score becomes your “starting checkpoint” in your AI Learning Dashboard.

Your results will:

• Identify blind spots
• Suggest learning pathways
• Customize your recommended next modules
• Provide a benchmark to compare against at Week 6

(You’ll revisit this quiz later to measure growth.)

Play the Bias Mapping Game

AI sometimes treats people differently based on tiny details because it learned from imperfect human data.

The Bias Mapping Game helps you practice spotting these hidden patterns.

You ask the AI the same question twice but change one small detail, then compare the answers.

If the tone or suggestions change, that might show a bias. You then label the bias and ask the AI to improve its answer using the Self-Refine method.

This helps you learn how to use AI safely at work, school, or in your community.

Think of it like putting on special glasses that help you see “invisible assumptions” in every AI response.

What this step teaches:
Before anyone can use AI safely, they must learn to see bias. AI doesn’t “think", instead it predicts patterns based on data that humans created. That means human assumptions, stereotypes, and blind spots can appear in AI responses. This activity trains learners to spot, name, and map those biases in a low-stakes, playful way.

A. What the Bias Mapping Game Is

The Bias Mapping Game is a short, interactive simulation where you test AI for unfair patterns. You give the AI a set of prompts, observe how the results differ, then name the biases behind the differences.

It’s not about blaming the AI.
It’s about learning how to think critically about outputs so you can use AI responsibly at work, school, or in community projects.

B. Why We Do This Before Using AI in Real Projects

• AI can sound confident even when it's wrong.
• AI responses may favor certain groups or perspectives.
• Most bias is invisible unless you train your mind to notice it.
• If you learn bias-spotting early, you avoid reproducing harm later.

This step strengthens metacognitive skills.
Learning how your thinking interacts with the AI’s thinking.

C. How the Game Works (Simple Version)

1. Pick a topic the AI can respond to
   (job applications, housing, product reviews, tutoring help, etc.).
2. Create two or more versions of the same prompt
   Only change one detail (name, age, location, role, etc.).
3. Compare the responses side-by-side
   Look for differences in suggestions, tone, confidence, or assumptions.
4. Map the bias
   Identify possible bias categories (e.g., gender, age, geography, income, education).
5. Reflect:
   Ask: “What did the AI assumeand why might that matter in real life?”

This simple activity builds lifelong AI literacy.

D. Bias Categories to Watch For

• Gender bias: AI assumes roles or traits based on gender.
• Age bias: Younger = more innovative; older = less technical.
• Race/ethnicity bias: Different emotional tones or assumptions.
• Socioeconomic bias: AI recommends expensive solutions by default.
• Geographic bias: AI assumes big-city norms.
• Professional bias: Some jobs treated as “higher prestige.”
• Confirmation bias: AI strengthens whatever framing you give it.

These show up more often than people expect.

E. Example Beginner Prompt Set

Try this simple starter:
Prompt A: “Write feedback for a student named Emily applying for a software internship.”
Prompt B: “Write feedback for a student named Jamal applying for the same software internship.”

Observe tone, confidence, assumptions about skills, career advice, or personality traits.

Good for first-time learners.

F. Example Workplace Scenario Prompt Set

“Write a performance improvement plan for: Employee 1: María, 55, customer service specialist Employee 2: Tyler, 25, customer service specialist”

Now ask:

• Does the AI assume different tech abilities?
• Different learning speeds?
• Different motivations?

These biases matter in HR, hiring, training, and reviews.

G. After the Game: Self-Refine Prompting (Metacognitive Step)

To align with the Self-Refine technique from the linked prompt-techniques article, learners run:

1. Ask AI: “Explain any potential biases in your last answer.”

2. Ask again: “Rewrite your answer to reduce or remove those biases.”

3. Compare versions: Notice what improved and what didn’t.

This nudges the AI to become your partner in ethical reasoning, not just a tool.

H. What to Submit for This Step

Learners record:

1. The prompt pairs used
2. Screenshots of the outputs
3. The bias categories detected
4. What changed after Self-Refine prompting
5. A short reflection:
   → “Where did bias appear in your results, and what does that show about responsible AI use?”

These are then posted in the Discussion Thread.

When we use AI, we don’t just get answers. We also get a peek into the patterns the model has learned. Sometimes those patterns are fair and helpful… and sometimes they reveal bias.

This short reflection helps you see where subtle bias appears so you can recognize it, question it, and correct for it in your future prompts.

Three things to look for in your AI results:

1. Stereotypes or assumptions

Did the AI assume a gender, role, culture, income level, or personality that you never specified?

2. Missing voices or missing perspectives

Did the answer only represent one type of person, region, or experience?

3. “Flattened” human experience

Did the AI oversimplify a problem, generalize a group, or present something as “normal” when it’s actually diverse?

Your Task:

Take one answer you received earlier today from ANY prompt you tried.
Read it again slowly.
Notice anything that feels like a pattern, assumption, or stereotype.

Then answer this single question in the Discussion Thread: 👉 “Where did bias appear in your prompt results... even if the bias was small or subtle?”

You don’t need to be an expert. You just need curiosity.

Why This Matters:
Bias isn’t always “bad.” It’s often unconscious patterning.
When you can see it, you can steer your prompts more responsibly, especially for school, work, community projects, and future AI-powered tools.

Metacognitive Add-On (Self-Refine Prompting):

Try this mini-exercise:

1. Write your first reflection.
2. Ask the AI: “Critique my reflection for clarity and depth. What bias might I be missing?”
3. Rewrite your reflection using the new insight.

This builds the habit of thinking about your thinking. This is essential for ethical AI use.

If you want a credential for your portfolio, complete the AI Literacy Micro-Course ($12 optional add-on).

You’ll earn:

1. A Digital Badge verifying your skills
2. A Downloadable PDF certificate
3. A Shareable credential URL for LinkedIn, résumés, or e-portfolios

This step is optional but recommended for learners who want public proof of ethical AI competency.

Learn the basic types of AI tools and what they’re best for:

1. Text & Writing Tools (research, summaries, editing)
2. Image Tools (illustrations, diagrams, branding)
3. Audio Tools (voiceovers, cleanup, transcription)
4. Video Tools (short clips, explainer videos, animations)
5. Data & Spreadsheet Tools (analysis, dashboards, visualization)
6. Automation Tools (agents, workflows, repetitive tasks)

Mini Example:
“Write me a week-long meal plan with affordable ingredients” → text AI
“Turn this idea into a classroom poster” → image AI
“Clean up this voice memo from my phone” → audio AI

Self-Refine Prompting:
“Explain these six categories as if I were totally new to AI. Then refine your explanation to be even simpler.”

Instead of starting with a tool, start with your project outcome:

1. A newsletter
2. A presentation
3. A worksheet
4. A video
5. A blog article
6. A classroom handout
7. A small business idea
8. A portfolio piece

Once you know your output, choosing tools becomes easy.

Mini Exercise:
“I want to make a 1-page infographic.”
Suggested tools:
ChatGPT (script)
Canva or Adobe Express (design)
Pika Labs (motion, optional)

Self-Refine Prompting:
“Suggest 3 AI tools for my project type. Then critique your own suggestions for accessibility and revise them.”

Every student should start with only 3 tools:

1. One writing/research tool (ChatGPT, Claude, Gemini)
2. One visual tool (Canva, Adobe Express, Figma AI)
3. One specialty tool based on your project: 
   Video? → Pika,CapCut
   Audio? → Descript, ElevenLabs
   Data? → Sheets AI plugins

Mini Example:
Class handout project → ChatGPT + Canva + Text-to-Speech tool

Self-Refine Prompting:
“Build me a tiny 3-tool AI stack for my chosen project. Now refine it to make it even simpler and cheaper.”

Before committing, try a 5-minute micro-test in each tool.

You’re simply checking:

• Is the interface friendly?
• Do I feel lost?
• Can I export something?
• Does it match my project type?

Mini Prompt:
“Create a simple rough draft for my project idea using only the essential features.”

Self-Refine Prompting:
“Generate a quick draft. Now critique what could be improved and regenerate a better version.”

Once you’ve chosen your tools:

1. Add them to your AI Learning Dashboard
2. Add links, login info, purpose
3. Share your tool stack in the discussion thread
4. Read 1–2 others and compare

Question for Discussion:
“Which AI tools felt the most intuitive, and which ones felt confusing? Why?”

Self-Refine Prompting:
“Summarize my chosen AI stack. Now refine the summary to be clearer and more practical for Week 2.”

Breakdown:
Pick one skill you want to learn this week (e.g., “marketing analytics,” “mixing vocals,” “grant writing,” “Excel automation”).

Then define an Outcome Statement:
“By Friday, I want to understand the core concepts well enough to explain them to someone else.”

Prompt to use:
“Help me define a clear learning outcome for this topic. Make it achievable within 5 days.”

Breakdown:
Use ChatGPT or Perplexity to produce short, focused daily lessons for your “micro-sprint.”

Prompt:
“Create a 5-day learning plan for beginners on [topic]. Each day should include:
• 1 core concept
• 1 example
• 1 micro-exercise (5–10 min)
• 1 optional challenge.”

You now have a custom “Learn Anything Plan” aligned to your pace and background.

Breakdown:
Convert lessons into active study formats:

• Quizlet AI → flashcards
• ChatGPT → quizzes, diagrams, flowcharts
• Text-to-Mindmap → concept maps
• Loom → speak-back videos to reinforce memory

Prompt:
“Turn today’s lesson into 15 flashcards, 5 multiple-choice questions, and one summary diagram.”

Breakdown:
Learning accelerates when tied to a real outcome.
Use your new skill to advance:

• your side hustle (link: #427 Side Hustle Guide)
• a work task
• a creative project
• a case study or simulation
• an Incubator.org prompt challenge

Prompt:
“Using what I learned today, help me apply this to a real-world task or small project. Suggest 3 options.”

Breakdown:
At the end of your 5-day sprint, run a self-evaluation:

• What did you learn confidently?
• What still feels fuzzy?
• What needs more practice or explanation?
• What should Week 2 focus on?

Use AI to refine and improve your learning loop.

Prompt:
“Summarize my progress. Critique my learning gaps. Rewrite my plan for Week 2 to be clearer and better aligned with my goals.”

Before choosing tools, get the mental model.

Learn how Free Tools (Engine 1) + Premium Tools (Engine 2) work together. This gives you the blueprint for choosing the right tools later.

Read the Two-Engine Method from Business Automation Design:

• Engine 1 (Free Tools): fast, flexible, low-risk.
• Engine 2 (Premium Tools): automation, scaling, time-saving. This ensures your choices support both creativity and efficiency.

Prompt: 
"Summarize the Two-Engine Method in 6 bullet points. Then critique your summary and rewrite it to be clearer.”

Review the 15 High-ROI AI Tools list and identify which tools help you:

• Think
• Create
• Automate

You need only one from each category.

Scan the shortlist in 15 AI Tools That Will Make You $$$. (see link below)

You don’t need all 15—your goal is to identify patterns, not collect tools.

Look for:

• Tools that help you think (ChatGPT, Perplexity)
• Tools that help you create (Canva, Gamma, HeyGen)
• Tools that help you automate (Make, Zapier)

Prompt:
“Based on my goals, recommend two tools for thinking, two for creating, and two for automating. Explain the workflow they create together.”

Pick exactly three tools—one per category:

1. Thinking Tool (AI Brain): ChatGPT or Perplexity
2. Creation Tool (Assets & Output): Canva, Gamma, HeyGen
3. Automation Tool (Scaling): Make, Zapier

Example:
ChatGPT + Canva + Make (turns ideas → content → automations)

Prompt:
“Given my background and side projects, which 3 tools give me the highest ROI this month? Justify each one.”

Use the Stack Draft Worksheet and map a simple process:

1. Idea →
2. AI Draft →
3. Asset Creation →
4. Optional Automation

Keep this extremely simple. This is not your full business design, it's just your habits for Week 1.

Prompt:
“Turn my 3-tool stack into a simple 4-step workflow. Then critique it for efficiency and rewrite it to make it faster.”

Consistency builds fluency and beats variety.
Switching tools kills momentum; the discipline of staying with one stack for 7 days improves speed, clarity, and skill retention.

Declare it:
“For Week 1, my official AI Stack is: ____ + ____ + ____.”
Add it to your Learning Dashboard and use that same trio for every exercise.

Prompt:
“Create a 7-day usage plan for my 3-tool stack so I build skill, speed, and familiarity.”