deciqAI Feynman Technique
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Overview
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."
Compose with neighbors: first-principles supplies the ground-level understanding Feynman Technique then tests. metacognition monitors your thinking process; Feynman Technique stress-tests the output. critical-thinking evaluates someone else's claimed understanding.
When to Use
- Need to know whether understanding is genuine vs. surface; preparing to teach or make a high-stakes decision; a model is giving wrong predictions
- Someone says: "explain it simply," "teach me like I'm five," "do you really understand this," "what am I missing?"
- Cutting through AI hype: "do I actually understand transformers / embeddings / RAG / agents, or am I just dropping the jargon?"
When NOT to use: Fast decision on a concept already tested; concept too new with no source material for Step 3; concept irreducibly formal — use first-principles instead; evaluating creativity or judgment, not understanding.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific concept to test → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- What it is. The Feynman Technique is an understanding test: explain a concept in plain language as if teaching a beginner; every breakdown point is a map of what you don't actually understand.
- Check fit — if irreducibly formal, redirect; if you need speed, skip.
- Elicit the specific concept. "I want to understand things better" is not workable; "I want to test whether I understand compounding interest" is.
[WAIT — do not advance until user responds]
- One step at a time. Walk through each step with their actual concept; identify breakdown points together; locate source material for gaps.
[WAIT — do not advance until user responds]
- Close by naming the specific gap most surprising — the thing the user thought they understood but the test revealed they did not.
[WAIT — do not advance until user responds]
The Process
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.
- Choose the concept and write its name. One specific concept, not a topic. "Compounding interest" is a concept. "Finance" is not.
- 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.
- 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.
- 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.
Output template
Feynman Understanding Audit: <concept>
Initial Explanation: <verbatim, unedited>
Gap Diagnosis: location | type (circular/jargon/unjustified/hedge) | specific question I cannot answer
Sources consulted per gap: gap → source → what it clarified
Refined Explanation: <revised>
Analogies: analogy | works when | breaks down when
Summary: genuine (can reproduce/predict/extend) | surface only | irreducible core
→ Method in Action: Feynman and the Challenger O-Ring Investigation (1986) · The Freshman-Lecture Test and Spin-Statistics (1961–1963) → 2026 lens: Feynman-Testing the AI Jargon: Transformers, Embeddings, RAG, Agents (2024–2026)
Feynman Audit Packs
| Domain | Surface recognition | Genuine understanding |
|---|---|---|
| Tech/engineering | Correct acronym use without explaining what problem each solves | Can predict failure modes and tradeoffs |
| Finance/investing | Fluent "DCF," "beta," "convexity" without explaining why formulas break down | Can explain to a non-finance person; spots when standard formulas give wrong answers |
| Leadership | Fluent framework use without explaining what behavior change each produces | Describes a concrete situation where each predicts a specific outcome |
Contribute a Feynman Audit Pack: one file cataloguing the top 5–10 surface-recognition patterns and what genuine understanding looks like.
Applying It Well
- The gap is the output — not the explanation. Treat each gap as a precise instruction for what to study.
- Do not edit the initial explanation in real time — editing papers over gaps. Write first; diagnose second.
- Circular definitions are the most common gap — circle every term appearing in its own definition.
- "Basically" and "essentially" are gap markers — mark every hedge; they signal recognition substituted for understanding.
- Return to primary sources for gap-filling — a secondary summary may contain the same gap.
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "I know what it means, I just can't explain it simply." | This is the definition of surface recognition. If you understand it, you can explain it simply. |
| [D] Replacing jargon with different jargon. | "Capital allocation efficiency" for "return on investment" is not simplification. Test: can someone with no domain background follow it? |
| [D] Producing a correct-sounding analogy that predicts nothing. | An explanation producing no testable predictions has not conveyed genuine understanding. |
| [D] Treating it as a communication exercise. | The goal is to find where you cannot explain — not to produce a good explanation. |
| [D] Stopping when the explanation "sounds good." | A fluent jargon-reduced explanation ≠ genuine plain-language explanation. Test: does it predict outcomes and failure modes? |
| [D] Filling gaps with a secondary summary that has the same gap. | If you still cannot explain Y after reading "X works by doing Y," the gap is still open. Chase to a primary source. |
| [D] Accepting "it's complicated" as a valid stopping point. | It is never a conclusion — it is the beginning of gap diagnosis. |
| [D] Using the technique on too large a topic. | Identify the smallest falsifiable unit: a mechanism, a principle, a formula's derivation. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- "Plain language" explanation contains undefined technical terms
- No gaps identified — concept is trivial or diagnosis step was skipped
- Analogies not tested against failure conditions
- Gaps filled by secondary summary that "confirmed" original explanation
- "Basically," "essentially," or "kind of like" in refined explanation without unpacking
- Explanation cannot predict failure modes or boundary conditions
Verification
- Single specific concept chosen and named (not a general topic)
- Initial explanation produced verbatim, without real-time editing
- Each gap categorized: circular / jargon / unjustified / hedge
- For each gap: specific question that cannot be answered was named
- Gaps filled from primary sources, not secondary summaries
- Refined explanation tested: can someone with no domain background follow it?
- Analogies have explicit stated breakdown conditions
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/feynman-technique · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/feynman-technique.json