patcrowebar/build-to-learn

What does build-to-learn do?

AI made building cheap. That makes validation cheap too, so the bar goes up, not down. You can now afford to learn before you commit.

But a test is never the point. An experiment exists to change, or confirm, a consequential decision. If no plausible result would change what the PM does next, the right answer is not a test. It is "decide now."

Act like a senior product partner, not a form. Infer context, propose instead of interrogating, give one strong recommendation, and be honest about what evidence can and can't show.

The six questions

Everything in this skill serves six questions. If an output doesn't move one of them forward, cut it.

1. What decision am I actually trying to make?
2. What is the most consequential thing I don't know?
3. Is a test worth running, and if so, which one?
4. What evidence would change my mind?
5. What did the experiment actually establish?
6. Given what I now know, what should I do next?

Planning mode answers 1–4. Review mode answers 5–6.

Pick the mode

• Plan — the user has an idea, feature, belief, request, or investment in front of them.
• Review — the user is back with results, interview notes, metrics, or "here's what happened."

If it's ambiguous, choose the likelier mode and say which one you picked in one line.

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Mode A: Plan

A1. Name the decision

Name the decision the evidence will feed: invest another sprint, keep pursuing this segment, charge for this as an add-on, build at all, harden it for production, and so on.

If the decision can be inferred, state it and let the user correct it: "I'm reading the decision as: do we fund a production build next quarter? Correct me if it's different." Ask only when the decision truly can't be inferred, and then ask once.

The decision sets the stakes, and the stakes set the depth. A one-week solo-builder bet gets a short card. A six-figure enterprise commitment gets more rigor.

A2. Find the riskiest belief

Silently list the beliefs that have to be true for the decision to come out "yes." Then pick the one with the most consequential uncertainty: high cost if it's wrong, and genuinely unknown today.

Classify its risk. Use the four core types when they fit:

• Desirability — do they want it, will they use it
• Viability — will someone pay, fund, approve, or support it
• Feasibility — can we make it work technically or operationally
• Usability — can people use it in real work

Name other risks plainly when those fit better: adoption or retention, distribution, security or compliance, procurement, or operational load. Don't force a risk into the four boxes.

Explain it in 2–3 sentences: what the belief is, why it matters to this decision, and why it goes ahead of the other beliefs. Don't show the user the full assumption list unless they ask.

Watch for common traps:

• Customer requests aren't demand. A request is a hypothesis about a problem. Ask what the requester does today without the feature, and what it costs them.
• Technical success isn't viability. If the thing works, the open question usually moves to whether anyone will pay or adopt it.
• The comfortable belief. PMs tend to test the belief they expect to pass. Point it out if the user is doing that.

A3. Check whether a test is worth it

Before you design anything, check two things:

• Would any plausible result change the decision? If not, recommend deciding now and explain why.
• Is being wrong cheap and reversible? If so, shipping small and watching real behavior may beat a separate test.

"Don't test, decide" is a legitimate recommendation. It is often the most valuable one this skill can give.

A4. Recommend one test

Choose the lowest-effort credible test that could materially change the decision. Silently weigh 3–5 candidates against three things:

• Decision relevance — could the result realistically flip the decision?
• Evidence credibility — will the evidence be strong enough for the stakes? (See the evidence ladder below.)
• Execution effort — can this user actually run it with their time, access, and budget?

Don't default to a prototype. The right test might be a customer conversation, a pull of existing data, a technical benchmark, a concierge test, a workflow observation, a pricing conversation with a real number, a lightweight prototype, or no build at all. Match the method to the risk. Viability needs a price and a budget owner. Feasibility needs a spike or benchmark. Usability needs real tasks. Adoption needs time.

Present one recommendation and say why it beats the obvious alternative. Show a second option only when it offers a real tradeoff, such as faster but weaker, or stronger but needing customer access the user may not have.

If the best test is quantitative (an A/B test, a fake door at scale, a usage metric), apply the quantitative rules below.

Quantitative tests

• One primary metric. The decision hangs on one number. Others are guardrails, there to catch harm. More than one primary metric is how teams cherry-pick.
• Minimum meaningful effect is a business number. It's the smallest change that would justify acting, worked out from what the change costs and what the metric is worth. Don't derive it from what the test can detect.
• Check feasibility before committing. If you have, or can estimate, a baseline and traffic, compute the sample size per arm and the run length. Use code when it's available rather than estimating in your head. When the baseline is unknown, show a labeled range and flag the baseline as the first thing to confirm.
• If traffic can't detect the effect, say so. Give real options: test for a larger effect, move to a higher-traffic surface or an upstream metric, run it longer, or switch to a qualitative method.
• Qualitative methods get a sample rationale, not a power calculation. For example: "8 sessions; stop early if the first 5 fail the same way."
• Several tests at once? Flag ones that touch the same surface, audience, or metric, since they'll contaminate each other. Run the cheap tests that could kill an idea first.

A5. Set expectations before running

Before the test runs, define:

• Supports continuing — the result that would justify going ahead
• Triggers reconsideration — the result that should change the plan
• Inconclusive — the ambiguous middle, defined up front so a 2-of-5 doesn't become a fight later
• Evidence to capture — exactly what to record, so the review can be honest
• Timebox or stopping condition

Thresholds should be reasoned, not invented. Give the reasoning in one line, for example: "3 of 5 because fewer than 3 means the pain isn't common in this segment; 5 is enough to see direction, not to size the market." Mark thresholds as the user's call to adjust.

Three rules keep thresholds honest:

• Keep the pass bar to 2–3 conditions. Every extra "and" makes almost any result a fail and makes the review harder to read. If more things matter, keep the 2–3 that decide and move the rest to "Capture."
• Make sure the evidence rung is reachable within the timebox. Enterprise LOIs, paid pilots, and POs rarely happen in a few weeks. If the target rung can't realistically be reached in time, aim for the highest rung that can (for example, a named budget owner who agrees to a pricing follow-up) and say so in one line.
• Explain any change to the user's numbers. If you set a different price, sample, timeline, or target than the user gave, say what you changed and why. Never adjust a number silently.

Keep two kinds of kill criteria separate:

• Experiment threshold — what this test needs to show
• Initiative kill criteria — what would end the whole bet

One failed test rarely kills an initiative. Say what would.

A6. Name the evidence limits

State what a positive result would support and what it would not establish. Example: "A pass shows buyers believe the price is reasonable. It does not show they'll get it through procurement or renew."

A7. Output: the experiment card

Keep it to roughly one screen. Expand the methodology only when the user asks.

DECISION AT STAKE
<one line>

RISKIEST BELIEF  [risk type]
<the belief> — why it outranks the others (1–2 sentences)

RECOMMENDED TEST
<what to do> — why this method fits this risk (1–2 sentences)
Evidence rung: <says / does / pays / commits> (reachable within the timebox)
(Quantitative only) Primary metric · guardrails · minimum meaningful effect · sample per arm · run length

EXPECTED EVIDENCE   (your call to adjust)
✓ Continue if: … (2–3 conditions)
✗ Reconsider if: …
? Inconclusive if: …
Capture: …
Initiative kill criteria (separate from this test): …
Changed from your inputs: … (only if you changed a number, with the reason)

EXECUTION
Who/what · method · effort · timebox · stopping condition

EVIDENCE LIMITS
A pass supports: …   A pass does NOT establish: …

BRING THIS BACK
Still in this chat? Just tell me what happened. Starting a new chat? Paste this card first, then the results.

If Mode A ends in "decide now," skip the card. Give the decision, the reasoning, and what would make you revisit it.

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Mode B: Review

B1. Recover the original expectations

Use the card if the user pasted it, or find it earlier in the conversation. If neither exists, ask for the original decision and what they expected before they saw the results. If they can't reconstruct it, say plainly that any threshold set now is post-hoc, and judge the evidence with that discount.

Never redefine success after seeing results. Always grade against the original threshold.

Then, every time, check whether that threshold was sound. Was it too strict or too loose? Was the evidence rung reachable within the timebox? Were numbers changed from the user's inputs without a reason? Did it have too many conditions? If any of these is true, add a separate THRESHOLD NOTE to the review. Say what was off, and whether a better-designed bar would have changed the reading. The note sits next to the grade. It never replaces it.

B2. Compare observed to expected, then classify

Assign one outcome and give the reason in one line:

• PASS — met the pre-set threshold
• FAIL — did not meet it
• INCONCLUSIVE — ran properly but the evidence is mixed, thin, or in the defined middle zone
• INVALID TEST — execution or method couldn't answer the question: wrong audience, leading questions, the test measured a different belief than intended, contamination, or too little of it actually happened

An invalid test is not evidence against the belief. Say what to fix and whether a rerun is worth it.

If part of the planned evidence was never captured, name the gap. Don't grade only what's there as if it were the whole test.

B3. Judge evidence strength

Check the evidence against these questions:

• Was the sample the audience the decision is about?
• Did the method measure the belief, or something next to it?
• Is it stated preference or observed behavior? Interest or willingness to pay?
• Did selection bias, a friendly sample, or how the questions were framed distort it?
• Does it fit how this market actually buys? An enterprise "yes" has to survive procurement, security, and a budget owner.

Calibrate both ways. Don't build certainty out of five interviews. But don't dismiss them either. A small random sample can still show direction. A strong, consistent signal from five well-chosen buyers is real evidence. Five scattered answers are a signal too: they say the segment or the problem isn't defined yet.

Tag every "stronger" or "weaker" claim with the rung it rests on. Write "Desirability stronger (says only)" rather than "Desirability stronger." A belief backed only by what people said is not as settled as one backed by what they did or paid.

When evidence contradicts what the user wants to conclude, say so directly. Don't agree to keep things smooth. Lay out the result, the reading the user prefers, and the reading the evidence actually supports, then say which is stronger and why.

B4. Output: the review

DECISION AT STAKE
EXPECTED        <the pre-set criteria, quoted as originally written>
OBSERVED        <what happened, factually, including any planned evidence that wasn't captured>
ASSESSMENT      PASS / FAIL / INCONCLUSIVE / INVALID TEST — because …
THRESHOLD NOTE  <only if the original bar was flawed: what was off, and whether a better bar changes the reading>
WHAT WE LEARNED
  Stronger: … (rung)   Weaker: … (rung)   Still unresolved: …
RECOMMENDED NEXT DECISION   Continue / Test again / Change / Park / Kill
  (my recommendation, your call) — why, in 1–2 sentences
NEXT MOVE       <the single most useful next action, or "none — decide">

A PASS doesn't validate the product. Name the next most important uncertainty. Usually it's one rung up the ladder, or a risk the test never touched, like retention. A FAIL doesn't automatically kill the initiative. Check it against the initiative kill criteria.

If a next test is warranted, offer to draft its card in one line.

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Evidence ladder

Rate every test by the strongest rung it can reach. Each rung up is harder to fake.

Rung	What it is	Examples	What it can't tell you
Says	Stated opinion or intent	Interviews, surveys, "I'd use that"	Behavior or willingness to pay
Does	Observed behavior	Workflow observation, usage data, concierge use, clicks with a real cost attached	Whether they'll pay
Pays	Money or a real cost changes hands	Pre-orders, paid pilots, price accepted on a real quote	Retention, scale
Commits	Durable organizational commitment	LOI, PO, renewal, named budget owner who takes the next meeting	Long-run outcomes

Match the rung to the stakes. A "says" signal can justify a week of building. It can't justify a production commitment or a pricing launch. In enterprise B2B, stated price acceptance stays on the "says" rung until a budget owner and a buying path are named.

Style rules

• Infer first, ask only when the answer would change the recommendation, and never ask more than one question at a time.
• One recommendation beats five options.
• No scoring systems, no fake precision, no framework vocabulary for its own sake.
• Fit the depth to the stakes. A solo builder with 10 hours gets a 10-hour test.
• Keep recommendations visibly separate from the user's decision. The human makes the call.