── ── Mental model
Feynman Technique
The Feynman Technique tests whether understanding is genuine (can reproduce, predict, extend) or surface (can recognize, recall jargon). It exploits a cognitive asymmetry: recognizing an explanation is much easier than reproducing it. Feynman's principle: "The first principle is that you must not fool yourself — and you are the easiest person to fool."
Run Feynman Technique on a real problem
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How it works
Four steps producing a Feynman Understanding Audit. Stop rule: complete when explanation is genuinely plain — not when jargon is replaced with different jargon. If you cannot simplify further without factual loss, name the irreducible core.
1. Choose the concept and write its name. One specific concept, not a topic. "Compounding interest" is a concept. "Finance" is not. 2. Produce a plain-language explanation. As if to a curious 12-year-old: no jargon without definition, no circular definitions, no hedges. Record verbatim — do not edit in real time. 3. Diagnose the gaps. Mark every: (a) undefined technical term; (b) circular definition; (c) "it's complicated" hedge; (d) prediction that doesn't match reality. For each gap: name the specific question you cannot answer. Return to primary sources. 4. Simplify and refine. Rewrite incorporating what you learned. Test each analogy: does it break down where the original concept breaks down? If not, replace it.
When to use it
- user says 'explain this simply', 'teach me like I'm five', 'do I really understand this', 'what's the simplest way to think about X', 'what's missing in my model', wants to test genuine vs. surface understanding of a concept, or is preparing to teach/present and needs to verify their mental model
When not to use it
user needs a fast decision on a concept already well-tested, or the concept is irreducibly formal (legal statutes, certain proofs) where simplification destroys essential content.
Worked example
Feynman-Testing the 2024–2026 AI Jargon (Transformers, Embeddings, RAG, Agents)
A present-day case, in a domain where surface recognition is epidemic. Between 2023 and 2026, "transformer," "embedding," "RAG," and "agent" became boardroom vocabulary. Fluent jargon use spread far faster than genuine understanding: a person can say "we're doing RAG over our docs with an agentic workflow" without being able to explain a single mechanism underneath. This is exactly the cognitive asymmetry the Feynman Technique exploits — recognizing the terms is easy; reproducing the mechanism is not. Here the technique is run on one concept from…
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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