AIYING: Four New Posts on AI Prompting, AI Basics, Learning by Doing and Meta Skills

AIYING | Four New Posts — From Better AI Answers to Better Learning

AI is changing not only what we can do, but also how we think, learn, and accomplish.

Four new AIYING posts explore practical ideas, each with a small learning opportunity.

1. Atomic Prompts #002 — Don't Accept AI's First Answer: Make AI Challenge Itself

Discover a simple prompt to question AI's first response, uncover weaknesses, and generate better alternatives.

https://dastikop.blogspot.com/2026/11/atomic-prompts-002-dont-accept-ais.html

2. I Am AI's Mind #003 — What Is a Token? How AI Breaks Language into Pieces

Understand how language models process text as tokens, why tokens matter, and explore tokenization through a hands-on experiment.

https://dastikop.blogspot.com/2026/11/i-am-ais-mind-003-what-is-token-how-ai.html

3. Learn as You Do #002 — Stop Preparing to Learn. Start Doing with AI.

Explore a different learning approach: begin with a real task, discover what you need to know, and learn while accomplishing it.

https://dastikop.blogspot.com/2026/11/learn-as-you-do-002-stop-preparing-to.html

4. Meta Skills #002 — Learning How to Learn: The Skill That Makes Every Other Skill Easier in the AI Age

Discover a six-step learning loop to develop transferable skills and become a more independent learner with AI.

https://dastikop.blogspot.com/2026/11/meta-skills-002-learning-how-to-learn.html

The common thread: Don't just consume AI-generated answers. Question, experiment, learn, and accomplish.

AIYING — From AI Awareness to Accomplishment.

#AIYING #GenerativeAI #AILiteracy #PromptEngineering #LearningByDoing #MetaSkills #AIFluency #LifelongLearning

Meta Skills #002 — Learning How to Learn: The Skill That Makes Every Other Skill Easier in the AI Age

AIYING | META SKILLS #002

Learning How to Learn: The Skill That Makes Every Other Skill Easier in the AI Age

When answers become abundant, knowing how to learn becomes more important than simply collecting answers.

What If Learning Itself Became Your Most Important Skill?

Imagine two people facing a technology they have never used before.

The first asks: "Where can I find a complete course?"

The second asks: "What can I try today, and what must I learn to make it work?"

Both approaches can be useful. But the second person has turned the immediate problem into a learning opportunity.

Generative AI makes this approach increasingly accessible. It can explain concepts, suggest experiments, provide examples, and help diagnose mistakes while someone works.

The interesting question is no longer only what do I need to know?

It is also: how can I become better at learning whatever I need next?

1. What Is a Meta Skill?

A meta skill is a capability that supports the development and application of other skills.

Examples include:

• Learning how to learn
• Asking productive questions
• Recognizing assumptions
• Evaluating evidence
• Adapting to unfamiliar situations
• Reflecting on experience

Knowing how to use a spreadsheet is a skill. Knowing how to approach an unfamiliar software tool is a transferable meta skill.

Knowing one programming language is useful. Knowing how to learn another language, test your understanding, and debug errors is a capability that can be reused.

In the AI age, these transferable capabilities deserve deliberate attention.

2. PO: Stop Learning Subjects. Learn Learning Moves.

PO: The most important thing to learn is not a subject, but how to move from not knowing to being able to do.

This provocation does not mean subject knowledge is unnecessary.

It invites us to examine the process through which unfamiliar knowledge becomes usable capability.

Instead of asking only which course to complete, we can ask which learning moves help us make progress.

A learning move is a small, deliberate action that changes our understanding or ability.

Examples include attempting a task, asking a better question, comparing alternatives, testing a hypothesis, explaining a concept, or correcting an error.

3. The Learning-to-Learn Loop

NOTICE → QUESTION → EXPLORE → TEST → REFLECT → TRANSFER

NOTICE — Recognize the learning gap

Identify something you want to accomplish but cannot yet do independently.

QUESTION — Define what you need

Turn uncertainty into a specific question.

EXPLORE — Use AI to investigate

Ask for explanations, examples, alternatives, and the minimum concepts needed to proceed.

TEST — Apply the explanation

Attempt the task and observe the result. Check important claims independently.

REFLECT — Examine your learning

Identify what worked, what failed, and what you understand better.

TRANSFER — Use it somewhere else

Apply the learning to a different situation. This helps reveal whether you have developed a reusable capability.

4. A Practical Experiment: Learn an Unfamiliar Tool

Suppose you want to create a simple visual dashboard but have never used an online diagramming application.

Open diagrams.net , a browser-based diagramming tool.

Your challenge is to create a diagram showing the six stages of the Learning-to-Learn Loop.

Step 1 — Attempt before requesting instructions

Explore the interface for three minutes. Try adding a shape, inserting text, and connecting two shapes.

Notice what you can accomplish without help.

Step 2 — Identify the gap

Write down one operation you cannot yet perform. For example:

How do I connect six shapes with arrows and align them neatly?

Step 3 — Ask AI for targeted assistance

Reusable Prompt 1 — Just-in-Time Learning

I am learning to use diagrams.net.

My immediate goal is to create a six-stage process diagram with connected shapes.

I can already add shapes and text, but I need help connecting and aligning them.

Give me only the next three actions. Explain the purpose of each action. Do not give me a complete tutorial.

Step 4 — Complete the diagram

Create the six stages: Notice, Question, Explore, Test, Reflect, and Transfer.

Connect them in sequence. If useful, add a final arrow returning from Transfer to Notice.

Step 5 — Test independent understanding

Without referring to the AI instructions, create a second diagram showing:

Problem → Idea → Experiment → Feedback → Improvement

If you can do this, you have begun transferring your learning beyond the original task.

5. Ask AI to Help You Reflect

Reusable Prompt 2 — Learning Reflection

I have just completed a task using an unfamiliar digital tool.

Ask me five short questions, one at a time, to help me identify:

• What I initially did not know
• What I discovered through experimentation
• What assistance I needed
• What I can now do independently
• Where I can apply the learning again

After my answers, summarize the reusable learning strategies I demonstrated. Do not invent achievements I have not reported.

6. The Difference Between Getting Help and Learning

There is an important distinction.

AI can complete a task for you without necessarily improving your ability to complete similar tasks.

If you simply copy the result, you may gain an output but little understanding.

If you inspect the process, ask questions, make changes, test the result, and repeat the task independently, you create opportunities for learning.

AI assistance creates an opportunity to learn. Independent capability is the evidence that learning has taken place.

7. What Might You Discover?

This experiment may reveal that:

• You already know more than you expected.
• A precise question can be more useful than a long general tutorial.
• Mistakes reveal specific learning needs.
• AI explanations must sometimes be adapted to the tool's current interface.
• Repeating a task independently is a useful test of practical understanding.

These are possible observations, not guaranteed outcomes.

8. Today's Accomplishment

Create two diagrams using a tool you have not used before.

The first should be completed with targeted AI assistance.

The second should be attempted independently.

Then record:

1. One new operation you learned
2. One mistake you corrected
3. One learning strategy you can reuse

You have not simply learned a diagramming tool. You have practised learning how to learn.

The Bigger Insight

Knowledge remains important. Foundations remain important. Expertise remains important.

But in a world where tools and technologies change rapidly, the ability to approach unfamiliar challenges becomes increasingly valuable.

The goal is not to eliminate preparation. It is to connect preparation more closely to meaningful action.

The most reusable learning outcome may be the ability to learn again.

AIYING — From AI Awareness to Accomplishment

Meta Skills for the AI Age explores transferable capabilities that help people learn, think, adapt, and accomplish meaningful work with AI.

AIYING | Ravindra Dastikop

Learn as You Do #002 — Stop Preparing to Learn. Start Doing with AI.

AIYING | LEARN AS YOU DO #002

Stop Preparing to Learn. Start Doing with AI.

What if your next learning experience began with an accomplishment you have not yet learned how to achieve?

From AI Awareness to Accomplishment

The Familiar Learning Sequence

We normally approach a new subject through a familiar sequence:

Study → Practise → Apply

First, we collect information. Then we practise what we have learned. Finally, we attempt something useful.

This sequence remains valuable, especially when foundational knowledge, safety, or professional competence is essential.

But for many everyday digital tasks, there is another possibility.

PO: Begin Before You Know How

Suppose we reverse the conventional sequence.

PO: Start the task before completing the learning required for it.

This is a deliberate provocation, not an instruction to ignore risks or necessary preparation.

Its purpose is to challenge one assumption: that learning must always precede application.

What becomes possible when we use AI to support learning during the task itself?

A Different Learning Loop

ATTEMPT → DISCOVER → LEARN → IMPROVE → ACCOMPLISH

The task becomes the starting point. Learning happens when a real need appears.

1. Attempt

Choose a small, achievable task, even if you do not know every step.

2. Discover

Identify what you cannot yet do. Your difficulties become learning questions.

3. Learn

Ask AI to explain only what is needed for the next step.

4. Improve

Apply the explanation and revise your work. Check the results rather than assuming the AI guidance is correct.

5. Accomplish

Produce a usable result and record what you learned while creating it.

Try It Yourself: Build a Simple Web Page

Imagine you have never written HTML. Instead of beginning with a lengthy HTML course, try creating a useful page.

Today's task: Create a personal learning dashboard containing three learning goals.

You need only a browser, a text editor, and an AI assistant.

Open ChatGPT and enter the following prompt.

Prompt 1 — Begin the task

I want to create a simple personal learning dashboard as a web page.

I have no prior HTML experience. Help me create a single HTML file with:

• A heading: My Learning Dashboard
• Three learning goals
• A checkbox beside each goal
• A short reflection section

Give me complete working code. Explain how to save it and open it in my browser. Keep the instructions beginner-friendly.

Step 1 — Generate the code

Read the instructions and copy the HTML provided by the AI assistant.

Step 2 — Create the file

Open Notepad or another plain-text editor. Paste the code.

Save the file as learning-dashboard.html. In Notepad, select "All Files" as the file type to avoid saving it with a .txt extension.

Step 3 — Open the page

Double-click the saved HTML file. It should open in your browser.

If something does not work, copy the relevant code and describe the problem to your AI assistant.

Step 4 — Learn what you just used

Prompt 2 — Learn from the work

Explain the HTML elements used in this dashboard.

Focus only on the concepts I need to understand and modify this page.

Show me which lines control the heading, learning goals, checkboxes, and reflection area.

Step 5 — Improve it yourself

Change the heading. Replace the example goals with your own. Add a fourth learning goal.

Save the file and refresh the browser.

You have now moved from using generated code to making your own changes.

Check Your Accomplishment

• Does the HTML page open in your browser?
• Are your learning goals visible?
• Can you select the checkboxes?
• Can you change the page content yourself?
• Can you explain at least three HTML elements?

A basic checkbox can be selected without saving its state between visits. Persistent progress tracking would require an additional feature.

What Have You Actually Learned?

You may have learned about HTML tags, file extensions, browsers, and basic page structure.

But something more important may have happened.

You discovered what you needed to learn because you were trying to accomplish something.

Your learning questions emerged from the task rather than from a predetermined syllabus.

The Bigger Insight

Do not wait until you know everything to begin something. Begin something small, and discover what you need to know.

Generative AI can make this approach more accessible by offering explanations, examples, feedback, and troubleshooting while we work.

But learning still requires attention, verification, experimentation, and reflection.

AI can help produce the first version. Your understanding grows when you inspect, question, modify, and test it.

Today's Accomplishment

Create one functioning personal learning dashboard and make at least one change without copying a completely new version from AI.

Record three things:

1. What I accomplished
2. What I learned while doing it
3. What I can now do independently

That is the beginning of cumulative AI-assisted learning.

AIYING — From AI Awareness to Accomplishment

Learn as You Do explores how practical tasks, reflection, and AI assistance can turn everyday accomplishments into opportunities for continuous learning.

AIYING | Ravindra Dastikop

Meta Skills #001 — Apply Before You Learn: The New Learning Skill in the AI Age

AIYING | META SKILLS SERIES #001

Apply Before You Learn: The New Learning Skill in the Age of AI

How lateral thinking and generative AI can transform learning from preparation for action into learning through action.

Series: Meta Skills in the Age of AI & Lateral Thinking

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1. The Learning Assumption We Rarely Question

For generations, education and professional development have followed a familiar sequence:

LEARN → PRACTISE → APPLY

First, acquire knowledge. Then develop proficiency through practice. Finally, apply what you have learned to solve a real problem.

This sequence has served us well. In many disciplines, particularly those involving safety, complex foundations, or professional responsibility, it remains essential.

But is it the only possible sequence?

Consider a graduate who wants to create an AI-powered application. The conventional route may begin with programming fundamentals, databases, APIs, machine learning, and software engineering.

Months later, the graduate might attempt to build an application.

Yet the desire to build something meaningful was present from the beginning.

Why must application always wait until learning is complete?

2. Enter Lateral Thinking: Challenge the Sequence

Edward de Bono's lateral thinking encourages us to examine established patterns rather than merely improve our performance within them.

One of its important devices is PO, a deliberate provocation that allows us to explore an idea without immediately judging whether it is correct or practical.

Let us apply this to learning.

PO: APPLY BEFORE YOU LEARN.

What if application were the starting point of learning rather than its final destination?

This is not a recommendation to abandon knowledge, foundations, or preparation.

It is a deliberate reversal intended to produce new possibilities.

Instead of asking:

"What must I learn before I can begin?"

We ask:

"What can I begin doing, and what must I learn to make progress?"

The change appears small. Its implications can be substantial.

3. Movement: From Curriculum-Driven to Accomplishment-Driven Learning

In lateral thinking, provocation is followed by movement. We do not stop at the unusual statement. We use it to discover a workable concept.

The principle emerging from our provocation is simple:

A desired accomplishment can help determine the learning journey.

Consider two approaches.

Conventional Sequence	Application-First Sequence
Choose a subject	Choose an accomplishment
Follow a predefined syllabus	Identify the knowledge needed
Practise selected exercises	Attempt a real task
Apply after preparation	Learn, test, and improve during application
Demonstrate acquired knowledge	Demonstrate both understanding and accomplishment

The second approach does not eliminate structured learning. It changes how learning needs are discovered and prioritized.

4. Generative AI Changes What Is Possible

Application-first learning is not entirely new. Apprenticeships, project-based education, experiential learning, and learning by doing have long demonstrated the value of practical experience.

What is new is the accessibility of generative AI as an interactive learning assistant.

AI can help a learner:

• Clarify the desired accomplishment.
• Identify missing knowledge and prerequisites.
• Break a complex task into manageable steps.
• Explain unfamiliar concepts when they become relevant.
• Generate examples and practice exercises.
• Review work and suggest improvements.
• Explore alternative approaches.
• Reflect on what has actually been learned.

Previously, a learner might have needed a teacher, mentor, textbook, and multiple resources to coordinate this process.

Generative AI can help bring many of these functions into one interactive workflow.

However, AI explanations and generated solutions can be incorrect. Learners must still verify important information, test results, and develop independent understanding.

5. A Practical Example: The Graduate Who Wanted to Build a Chatbot

Imagine a graduate with no programming experience who wants to build a chatbot that answers questions about a college course.

The conventional response might be:

"Learn Python, APIs, databases, and AI engineering first."

Our application-first response is different.

Begin with the smallest useful version of the chatbot.

Step 1: Define the accomplishment

The chatbot should answer five frequently asked questions using a small set of approved course information.

Step 2: Discover the learning requirements

Ask AI to identify the minimum concepts, tools, and skills required to build a simple prototype.

Step 3: Build a small version

Begin with a no-code prototype or a simple program, depending on the learner's starting capability.

Step 4: Learn at the point of need

When the learner encounters an unfamiliar concept, such as an API, the immediate task creates a reason to understand it.

Step 5: Verify understanding

The learner must explain how the chatbot works, what information it uses, where errors can occur, and what limitations remain.

Step 6: Improve the accomplishment

Test the chatbot with real questions, identify failures, and improve its responses.

The outcome is not merely a prototype.

The learner has also developed a process for discovering, acquiring, and applying knowledge.

That process is the meta skill.

6. The Deeper Meta Skill: Learning to Learn

Learning to learn is not simply the ability to remember information quickly.

It includes the ability to:

• Recognize what one does not know.
• Identify what is worth learning.
• Choose an effective learning strategy.
• Seek and evaluate appropriate assistance.
• Monitor progress and understanding.
• Transfer knowledge to unfamiliar situations.
• Revise the learning approach when circumstances change.

In the AI age, this capability becomes especially important because people may encounter unfamiliar tools, tasks, and disciplines more frequently.

Instead of preparing once for a fixed professional role, learners may need to develop new capabilities repeatedly throughout their careers.

We can describe this shift as:

FROM LEARNING A SKILL

↓

TO LEARNING HOW TO BECOME SKILLED

7. An AI Prompt You Can Use Today

Try this prompt with a small project you have wanted to undertake but have postponed because you believe you lack the necessary skills.

AIYING META SKILLS PROMPT #001

I want to accomplish the following:

[Describe your desired outcome]

My current knowledge and experience are:

[Describe your starting point]

Act as my application-first learning coach.

1. Help me define the smallest meaningful accomplishment.
2. Identify the essential prerequisites and any safety or foundational knowledge I must acquire before starting.
3. Break the accomplishment into five manageable steps.
4. For each step, identify only the knowledge and skills needed at that stage.
5. Explain unfamiliar concepts with simple examples.
6. Give me a practical task before providing a complete solution.
7. Ask me to explain what I have understood.
8. Help me test the result and identify mistakes.
9. At the end, summarize the skills I acquired and the gaps that remain.

Do not simply complete the task for me. Help me become capable of completing and explaining it.

8. A Five-Minute Meta Skill Experiment

You do not need to begin with a complex technical project.

Choose something small:

• Create a personal expense tracker.
• Build a simple web page.
• Analyse a small dataset.
• Prepare a presentation on an unfamiliar topic.
• Automate one repetitive spreadsheet task.

Spend five minutes defining the accomplishment and asking AI for the first practical step.

Then complete that first step, which may take longer.

Record three observations:

1. What did I discover that I needed to learn?
2. What did I accomplish?
3. What can I now do or explain without AI assistance?

The third question is especially important.

AI-assisted completion is not automatically evidence of learning.

The learner must demonstrate some increase in independent capability.

9. Judgment: When Application-First Learning Is Not Enough

A lateral provocation is useful because it opens possibilities, not because it must always be adopted literally.

Application-first learning works particularly well for exploratory projects, prototypes, familiar low-risk tasks, and situations where immediate feedback is available.

But there are important limits.

In medicine, aviation, electrical safety, cybersecurity, and other high-stakes fields, essential foundations and supervised training cannot simply be bypassed.

Even in software engineering, learners who rely entirely on AI-generated code without understanding its behavior may create unreliable or insecure systems.

The practical conclusion is therefore not:

"Never learn before applying."

It is:

"Do not assume that all learning must precede all application."

A more flexible learning model may alternate between preparation, application, reflection, and deeper study.

10. From Learning to Operacy

Edward de Bono introduced the concept of operacy to emphasize the capacity to make things happen through effective thinking and action.

In his work, thinking is not limited to analysis, criticism, or demonstrating that an answer is correct. It also involves designing possibilities, choosing courses of action, and accomplishing useful results.

This provides an important connection to our series.

Generative AI may help people access information and attempt tasks that previously required substantial preparation.

But the human challenge remains:

Can we define worthwhile outcomes, develop the necessary understanding, make sound judgments, and turn possibilities into responsible action?

Application-first learning can become one practical route toward developing that capacity.

11. The Meta Skill Advantage

Individual skills remain valuable. Deep expertise remains important. Foundational knowledge does not become irrelevant because AI can generate an answer.

What changes is the relationship between skills and the ability to acquire them.

Someone who knows one tool can perform tasks using that tool.

Someone who has learned how to learn can approach unfamiliar tools and develop new capabilities.

Someone who combines learning agility, lateral thinking, sound judgment, and AI assistance may be better equipped to move across changing professional situations.

This is the larger opportunity of meta skills.

THE META SKILL TAKEAWAY

Do not wait until you know everything before beginning something meaningful.

Begin with a suitable accomplishment. Discover what you need to know. Learn deliberately. Test your understanding. Improve through action.

The ultimate outcome is not merely completing the task. It is becoming more capable.

12. One Question to Carry Forward

Consider something you have been postponing because you believe you are not yet sufficiently prepared.

What is the smallest safe and meaningful part of it that you could begin today?

That question may reveal a new learning path.

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Coming Next: Meta Skills #002

Thinking About Thinking: Who Is Managing Your Thinking When AI Does the Thinking?

When AI can generate explanations, recommendations, and solutions, an important human capability is knowing when to question, redirect, or change the thinking process itself.

The next episode explores metacognition, deliberate thinking, and the role of human direction in AI-assisted work.

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About the Series

Meta Skills in the Age of AI & Lateral Thinking is an AIYING learning and thought-leadership series exploring the transferable human capabilities needed to learn, think, create, decide, and accomplish in an AI-enabled world.

The series draws inspiration from Edward de Bono's thinking methods and develops contemporary applications involving generative AI.

Original de Bono concepts and new AI-age interpretations are distinguished throughout the series.

AIYING
Think Differently. Accomplish More.

Conceptual References

• Edward de Bono — Lateral Thinking: A Textbook of Creativity.
• Edward de Bono — Serious Creativity.
• Edward de Bono — De Bono's Thinking Course.
• Edward de Bono — Po: Beyond Yes and No.

Editorial note: The application-first learning framework and its generative AI applications are contemporary interpretations developed for this series, not methods attributed directly to Edward de Bono.

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