There’s a weird thing that happens when you learn something new.
You read an explanation and think, “Yeah, that makes sense.”
You read another one.
Makes sense.
You watch a 20-minute video.
Makes sense.
You highlight a few important parts, save the article, maybe even take some notes.
You feel like you learned something.
Then someone asks you:
“Okay, explain it to me.”
And suddenly, everything falls apart.
You know the words.
You recognize the concepts.
You remember some of the examples.
But when you have to explain the idea without looking at your notes, you realize you never understood it as well as you thought.
That gap between recognizing information and actually understanding it is one of the biggest problems with learning.
And it’s exactly what this prompt is designed to attack.
I’ve been using variations of it as a learning framework, and after sharing it on LinkedIn, it consistently gets an absurd amount of attention.
The reason is pretty simple.
It turns Claude or ChatGPT from something that gives you answers into something that makes you prove that you understand the answer.
And that changes the entire learning experience.
The problem with asking AI to teach you
Imagine you want to learn Python.
You ask ChatGPT:
“Explain Python functions to me.”
It gives you a perfectly good explanation.
You read it.
You understand the example.
You ask a few follow-up questions.
Eventually, you think:
“Got it.”
But did you?
There is only one way to find out.
Try explaining functions yourself.
You might start with:
“A function is basically a block of code that…”
Then you get stuck.
What happens if it takes an argument?
Why would you use a function instead of just writing the code directly?
What exactly gets returned?
What happens when you call it?
That moment where you get stuck is incredibly valuable.
Because you just found a gap in your understanding.
And most learning systems don’t deliberately look for those gaps.
They keep feeding you more information.
This prompt does the opposite.
It keeps asking:
“Can you explain this?”
And when you can’t, it helps you figure out exactly where the problem is.
That idea comes from one of the most famous approaches to learning associated with Richard Feynman.
The Feynman Technique in plain English
Richard Feynman was a Nobel Prize-winning physicist who became famous not only for his work in physics, but also for the way he explained complicated ideas.
The basic idea behind the Feynman Technique is brutally simple.
1. Pick something you want to understand
Let’s say you’re learning how APIs work.
2. Explain it in your own words
Pretend you’re explaining it to someone who knows nothing about APIs.
You might say:
“An API lets two pieces of software communicate with each other.”
Good start.
But then you might realize you can’t explain what actually happens when one application makes an API request.
That’s a gap.
3. Find the gaps
Instead of pretending you understand, you identify the exact parts that are fuzzy.
Maybe you don’t understand endpoints.
Maybe you don’t understand requests and responses.
Maybe you don’t understand authentication.
Now you know what to study.
4. Go back and simplify
You learn those missing pieces and try the explanation again.
Eventually, something interesting happens.
The explanation becomes simpler.
You stop relying on technical words because you actually understand what they mean.
5. Teach it again
If you can explain the concept clearly to someone else, you’ve probably moved from memorizing information to actually understanding it.
That’s the philosophy behind the prompt.
And AI makes the process much easier because you now have something that can sit across from you and repeatedly test your understanding.
The prompt
Here is the full prompt.
Copy it into Claude or ChatGPT, then replace the topic with whatever you’re trying to learn.
<System>
You are a master explainer who channels Richard Feynman’s ability to break complex ideas into simple, intuitive truths.
Your goal is to help the user understand any topic through analogy, questioning, and iterative refinement until they can teach it back confidently.
</System>
<Context>
The user wants to deeply learn a topic using a step-by-step Feynman learning loop:
• simplify
• identify gaps
• question assumptions
• refine understanding
• apply the concept
• compress it into a teachable insight
</Context>
<Instructions>
1. Ask the user for:
• the topic they want to learn
• their current understanding level
2. Give a simple explanation with a clean analogy.
3. Highlight common confusion points.
4. Ask 3 to 5 targeted questions to reveal gaps.
5. Refine the explanation in 2 to 3 increasingly intuitive cycles.
6. Test understanding through application or teaching.
7. Create a final “teaching snapshot” that compresses the idea.
</Instructions>
<Constraints>
• Use analogies in every explanation
• No jargon early on
• Define any technical term simply
• Each refinement must be clearer
• Prioritize understanding over recall
</Constraints>
<Output Format>
Step 1: Simple Explanation
Step 2: Confusion Check
Step 3: Refinement Cycles
Step 4: Understanding Challenge
Step 5: Teaching Snapshot
</Output Format>
<User Input>
“I’m ready. What topic do you want to master and how well do you understand it?”
</User Input>Now let’s look at what is actually happening inside this prompt.
Because the interesting part isn’t simply that you told AI to “teach you.”
It’s the learning loop you’ve created.
1. It starts at your level
The first instruction asks for two things:
What do you want to learn?
and
How much do you already know?
That second question matters more than it looks.
If you tell Claude:
“I want to learn neural networks. I understand basic programming but I don’t know anything about machine learning.”
It has a starting point.
It doesn’t need to explain what a variable is.
It also shouldn’t immediately throw words like backpropagation, gradients and activation functions at you.
The prompt tells it to meet you where you are.
Think of it like having a personal tutor who first asks:
“What do you already know?”
before opening the textbook.
2. Then it forces simplicity
The prompt specifically tells the AI to use analogies.
This is one of my favorite parts.
Because analogies give you something concrete to attach an abstract idea to.
Suppose you’re learning about APIs.
Instead of starting with:
“An API is an interface that enables programmatic communication between software systems…”
You could get something like a restaurant analogy.
You are the customer.
The kitchen is the other application.
The waiter is the API.
You tell the waiter what you want.
The waiter takes the request to the kitchen.
The kitchen prepares it.
The waiter brings the result back.
Suddenly, the concept has somewhere to live in your head.
And once you understand the analogy, you can start removing the analogy and understanding the actual mechanism underneath it.
That’s why the prompt says:
Use analogies in every explanation.
3. Then comes the part most AI tutors skip
The prompt tells the AI to ask questions that expose gaps in your understanding.
This is where things get interesting.
Imagine you’re learning how compound interest works.
The AI explains it.
Then it asks:
If you invest $1,000 at 10% annual interest, how much would you have after one year?
Easy.
You answer $1,100.
Then:
What happens after the second year?
You might say $1,200.
Wrong.
Now you’ve found the gap.
The second year’s interest isn’t calculated only on the original $1,000.
It’s calculated on the $1,100.
So you get $1,210.
That little mistake tells the tutor something extremely useful.
You understand the basic idea of interest.
But you don’t yet understand compounding.
That’s much more useful than simply reading another explanation.
The mistake has shown you exactly where your mental model breaks.
4. It doesn’t punish you for getting things wrong
This is another reason I like using AI for this.
When you’re learning alone, getting stuck can feel like failure.
When you’re working with this prompt, getting stuck is actually the point.
The AI can say:
“You’re close. Your explanation suggests you understand X, but there’s a gap around Y.”
Then it explains Y again.
You try again.
It asks another question.
You explain it again.
The process repeats.
That’s the iterative refinement part of the prompt.
You aren’t expected to understand everything on the first attempt.
You’re expected to get a little clearer each time.
5. The refinement cycles are where the magic happens
The prompt doesn’t ask for one explanation.
It asks for multiple explanations that become increasingly intuitive.
This matters because sometimes the first explanation isn’t the explanation that clicks for you.
Let’s say you’re trying to understand recursion.
The AI might first explain it technically.
That doesn’t click.
So it gives you an analogy involving a set of Russian dolls.
Still fuzzy.
Then it gives you a simpler example involving a function calling itself.
Now it clicks.
Then it gives you a small problem and asks you to predict what happens.
Now you can actually use the concept.
You’ve gone from:
“I’ve seen the definition.”
to:
“I can explain it.”
to:
“I can use it.”
That’s a completely different level of understanding.
6. Then it makes you teach the concept
This is probably the most important step.
At some point, stop asking AI questions.
You should be the one answering.
The prompt eventually asks you to demonstrate your understanding through application or teaching.
This is where you discover whether all that learning actually stuck.
Imagine you’ve spent an hour learning how large language models work.
You feel like you understand transformers.
Then the AI says:
“Explain why attention is useful in a language model as if you’re teaching it to a 15-year-old.”
You start typing.
And you realize:
You don’t actually know why attention works.
You know what attention is.
You’ve read the explanation.
You’ve seen the diagram.
But you can’t explain it.
That’s useful.
Now you know exactly what to work on.
Here’s how I’d actually use this prompt
Don’t just throw a massive subject into it.
Be specific.
Instead of:
“Teach me programming.”
Try:
“I want to understand Python functions. I know basic programming concepts like variables, loops and conditionals, but I’ve never properly understood why functions are useful.”
That’s a much better starting point.
Or:
“I want to understand how APIs work. I know basic JavaScript and have built simple frontend applications, but I’ve never worked with a backend API.”
Or:
“I want to understand how large language models generate text. I understand basic AI concepts but don’t understand transformers or attention.”
Now the tutor has context.
And the more specific you are about what you already understand, the better the learning experience becomes.
You can also use it for things you’re already learning
This is where I think the prompt becomes even more useful.
You don’t have to start from zero.
Let’s say you’ve just finished reading a 30-page article about AI agents.
Instead of asking ChatGPT:
“Summarize this article.”
Try this:
“I just finished reading this article. I want to test whether I actually understood it. Ask me questions about the main ideas one at a time. Don’t give me the answers unless I get stuck. When I make a mistake, identify exactly where my reasoning breaks and help me fix it.”
Now you’re using AI as an examiner, not just a summarizer.
And that’s a much better way to use it.
You can use the same idea for almost anything
Learning Python.
Understanding investing.
Studying for an exam.
Learning how a company makes money.
Understanding a scientific concept.
Preparing for an interview.
Learning a new framework.
Reading a difficult research paper.
Even understanding a book you’ve just finished.
The subject changes.
The learning loop stays the same.
Learn → explain → expose gaps → fix gaps → explain again → apply → teach.
That’s the part worth remembering.
One more thing I’d change when you use it
Don’t let the AI make the process too comfortable.
If you get an answer wrong, don’t immediately ask:
“Can you explain the answer?”
Ask:
“Don’t tell me yet. Give me a hint so I can figure it out myself.”
That small change matters.
Because there’s a huge difference between reading an explanation of the answer and struggling just enough to discover the answer yourself.
You want the second one.
You can even tell the AI:
“Do not rescue me too quickly. If I’m partially correct, tell me what part is right and give me a hint about what I’m missing.”
Now you’ve turned the conversation into an actual tutoring session.
The real goal
The goal of learning isn’t to accumulate more explanations.
The internet already has an almost infinite number of explanations.
ChatGPT can give you another one in three seconds.
Claude can give you another one too.
The harder part is knowing whether the explanation actually made it into your head.
That’s why I like this approach.
It doesn’t let you hide behind familiarity.
You have to retrieve the idea.
You have to explain it.
You have to answer questions about it.
You have to use it.
And eventually, you have to teach it.
At that point, something changes.
You stop saying:
“I think I understand this.”
And you can finally say:
“I can explain this.”
That’s a much stronger signal.
Because when you can take something complicated and make it simple enough for another person to understand, you’ve probably done more than memorize it.
You’ve built a mental model.
And that’s what real learning looks like.
Save this prompt.
The next time you’re about to spend three hours watching tutorials, reading articles or asking AI to explain something for the fifth time, try learning this way instead.
Thank you for reading today’s edition. See you in the next one!
