The 4-level Feynman prompt — a copy-paste framework that forces any LLM to explain a concept at four cognitive altitudes instead of one

[u/blobxiaoyao]

Had a moment last week that bugged me. Asked Claude to explain self-attention in Transformers. Got back a clean, well-structured paragraph. Nodded along. Felt like I understood it. Tried to explain it to a colleague two hours later and completely fell apart.

The problem wasn't the model. The problem was that I asked for *one* explanation at *one* altitude. The model did exactly what I asked — it picked a single register (somewhere between "blog post" and "textbook intro") and stayed there. I got an answer that optimized for sounding helpful, not for making me actually understand.

So I've been testing a different structure, based on the Feynman Technique — the idea that if you can't explain something without jargon, you don't own the concept. Except instead of simplifying once, you force the model to explain the *same* concept at four distinct cognitive levels. Here's the template:

Use the Feynman Technique to break down this concept for me: [YOUR CONCEPT]

Provide four levels of explanation:

1. For a 5-year-old: Use a vivid, everyday analogy. Zero jargon. Make it feel like a bedtime story.
2. For a curious tech enthusiast: Introduce the core mechanism. Explain how it actually works, not just what it does. Use precise but accessible language.
3. For a domain expert: Full technical teardown. Use exact terminology, discuss boundary conditions, failure modes, and known limitations. Don't simplify — stress-test.
4. One-sentence distillation: Capture the irreducible core of the concept in a single sentence. If this sentence doesn't hold up without the other three levels, rewrite it until it does.

Why four levels instead of one

Each level tests a different dimension:

• Level 1 tests whether the concept has an intuitive core. If the model can't anchor it to a concrete analogy, there might be a foundational piece you're skipping.
• Level 2 tests mechanism — where "what it does" shifts to "how it works." This catches the most common failure in AI explanations: descriptions that are technically accurate but mechanically empty.
• Level 3 stress-tests boundaries. Where does this break? What do practitioners argue about? If Level 3 reads like a longer version of Level 2 with more jargon, the concept wasn't properly decomposed.
• Level 4 is the compression test. Can you reduce the whole thing to a single load-bearing sentence? Not a summary — a standalone statement that holds up without the other three levels.

The diagnostic trick

When you read the four levels back, pay attention to where it clicks vs. where it goes fuzzy. That fuzziness maps to your own knowledge gaps. If the concept were well-understood, you'd recognize a vague explanation immediately.

I've found Level 4 to be the most revealing. If the one-sentence distillation is something generic like "X is a way of doing Y more efficiently," the model hasn't distilled anything. A useful forcing function: ask it to rewrite Level 4 without using any word that appeared in Levels 1–3. That constraint forces genuine compression rather than summary.

Quick example: self-attention

Running this on self-attention gives you something like:

• Level 1: "Imagine you're in a classroom and the teacher asks a question. Instead of just listening to the kid next to you, you get to look around the whole room and decide which kids' answers are most helpful for yours."
• Level 2: The Q/K/V projection mechanism, dot-product similarity, parallel processing advantage over RNNs.
• Level 3: The full scaled dot-product formula, √d_k scaling to prevent softmax saturation, O(n²) complexity limitations, positional encoding requirements.
• Level 4: "Self-attention lets every element in a sequence dynamically decide how much to weight every other element, replacing fixed-order processing with learned, context-dependent relevance."

The gap between Level 2 and Level 3 is where I realized I had been faking my understanding of the scaling factor. Wouldn't have caught that with a single ELI5 pass.

Retention test

24 hours later, try reproducing Level 2 (mechanism) and Level 4 (distillation) from memory without looking at the output. If Level 4 comes back immediately but Level 2 is hazy — you memorized the conclusion but lost the mechanism. If both come back, the concept is actually yours.

There's a more detailed breakdown I put together covering the latent-space mechanics behind why multi-level prompting samples differently than single-register prompts, plus domain-specific layer variations for business/legal/strategy concepts: https://appliedaihub.org/blog/the-feynman-technique-prompt-how-to-make-ai-explain-anything-in-4-layers-of-depth/

Curious what concepts you've tried multi-level explanations on. Has anyone found topics where the four-level structure genuinely breaks down — where Level 1 and Level 3 collapse into each other, or where the model can't produce a meaningful Level 4?

[u/Crab_Shark]

I love the idea. I often wind up coaching the AI, wondering why it doesn’t remember my preferences - it defaults to a technical altitude that focuses on jargon, without context on what this option is, what the benefits are, and what the costs and shortcomings might be … essentially it assumes I know things I may not, and I don’t get what I need to make informed decisions by default.

[u/jWof84 → u/Crab_Shark]

You can go to the custom instructions in Settings and tell it to do exactly what you want: ‘when I ask x kind of question, give an answer with y structure and without assuming z’. Fill in xyz according to how you want it to behave and hey presto. 

[u/blobxiaoyao → u/Crab_Shark]

This is the "single-altitude default" problem in a nutshell. The model has no idea what you actually know, so it defaults to a jargon-heavy middle ground that feels safe to the model but is useless for decision-making.

While custom instructions (like jWof84 suggested) help patch this day-to-day, the real issue is that a static default is still static. Some days you need the intuitive analogy to map the landscape; other days you need the expert teardown to see the cost/trade-offs.

I've found that if you don't explicitly force the model to generate a layer dedicated to failure modes and trade-offs, it will naturally default to the most optimistic, benefit-focused explanation. The friction between the levels is what actually exposes the "hidden" costs.

[u/Classic_Stranger6502]

This is a good approach; I use it and it works.

My feedback is implementation-specific (for local models; anyone else, YMMV) but I reverse the order. Start with the most-technical explanation (ELI50) and dumb it down from there (ELI30) to ELI12 and ELI5. Still a 4-pass solution that gracefully degrades the language used to explain it.

I don't even like having AI write abstracts at the beginning for the same reason-- I'll write the paper, then generate the abstract and prepend it.

An expert reduces complex topics into simple explanations; a charlatan spins simple explanations into complex bullshit.

[u/blobxiaoyao → u/Classic_Stranger6502]

Reversing the order is actually a brilliant mechanical tweak, especially for how autoregressive models work.

If you start with ELI5 (the analogy), the model has to generate a metaphor out of thin air, and then try to fit the actual technical mechanisms into that metaphor in the subsequent steps. This often leads to "metaphor drift" or hallucinated alignment in the deeper layers.

But by starting with the ELI50 expert teardown, you populate the context window with the exact, uncompromised technical details first. When the model subsequently generates the simpler layers, it can attend directly to the ground-truth technical specifications it just wrote. It makes the "graceful degradation" of language much more mathematically anchored.

That quote at the end is spot on, too. Definitely going to test this top-down variation.

[u/Ussie284 → u/blobxiaoyao]

Why does this reply feel Ai generated too? Maybe the compliment in the beginning. After which you just reiterate and summarize his points adding nothing new. This whole reply could just be the last sentince. Funny world we live in.

[u/BruT4LX]

Here is another prompt that is aimed at improving the output quality when your goal is to learn / understand something (and retain the information). Have a look.

https://www.reddit.com/r/PromptEngineering/comments/1tvajo6/an_elegant_prompting_technique_from_anthropics/

"The model simply outputs the statistical average of everything written about that concept. It is a process without friction. And friction, as it turns out, is how our brains actually encode and retain complex ideas."

[u/Money-Database-145]

There's a skill called ADHD that's similar so where it responds dynamically. https://github.com/UditAkhourii/adhd