GuideBeginner

Product Discovery And Prototyping

Teaches how to validate a bet with real users before building it. Building is the most expensive way to learn; this guide buys cheap evidence about risky assumptions before spending weeks of engineering time. It covers continuous discovery as a habit, not a one-time phase; interviews that don't lie — asking about past behavior, not future intentions, following the Mom Test approach; the opportunity solution tree for mapping the problem before jumping to a solution; turning an assumption into a falsifiable hypothesis and designing the cheapest test that can refute it (the riskiest assumption test); prototyping at the right fidelity level — paper, clickable, wizard-of-oz, fake door; avoiding confirmation bias and the say-do gap; and synthesizing findings to decide whether to continue, pivot, or kill the bet. It picks up exactly where the Product Thinking guide left off: Mercado's `recommendations` bet, with its riskiest assumption already identified but untested. All the decision logic — which test to pick, how to weigh evidence, how to update confidence — runs as executed Node.js code. It closes with a capstone project that validates that bet end to end.

64
lessons
8
modules
English · Spanish
available in
Yes
certificate
Free
access
NIEVA

Outcomes

What you'll be able to do

  • Understand why building is the most expensive way to learn, and adopt discovery as a continuous habit, not a one-time phase
  • Interview without getting lied to: ask about past behavior, not future intentions (the *Mom Test*)
  • Map the problem space with the opportunity solution tree (outcome → opportunities → solutions → experiments) before jumping to a solution
  • Turn an assumption into a falsifiable hypothesis and design the riskiest assumption test: the cheapest one that can actually refute it, run with `pickTest`
  • Prototype at the right fidelity level: paper, clickable, wizard-of-oz, fake door — matched to cost and what it can test
  • Avoid the biases that ruin discovery: confirmation bias, leading questions, the *say-do gap*; weigh evidence by strength (observed behavior beats stated opinion)
  • Synthesize interview findings by counting signals, not noise, and update a bet's confidence with `updateConfidence`, connecting back to RICE's `confidence` input
  • Decide to continue, pivot, or kill a bet with evidence, applied end to end to Mercado's recommendations bet in the capstone project

Before you start

What you need to bring

It's for you if...

  • Engineers who already identified a bet's riskiest assumption in the Product Thinking guide and now need to validate it with real users
  • Devs who want to run cheap, fast validation before writing weeks of production code
  • Anyone who's watched a team ship a "validated" feature that was only ever validated by asking people what they'd like
  • Engineers who want a structured alternative to guessing, or to waiting for a PM or researcher to hand them the decision already made

Requirements and materials

  • Product Thinking for Engineers Guide completed (or equivalent: outcomes vs. outputs, RICE, finding a bet's riskiest assumption)
  • Comfort running simple Node.js scripts (no dependencies)
  • No prior user research or interviewing experience required — the guide teaches it from scratch

Content

The syllabus, module by module

Open any of them to see its lessons.

Common questions

What people usually ask

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