Minimum Viable Product (MVP)
// Definition
Minimum Viable Product is the smallest version of a product that lets you test a core assumption with real users and generate validated learning. The word "minimum" does not mean low quality — it means minimum scope. An MVP can be polished; it is simply not feature-complete. The concept is widely abused to justify shipping half-finished work under the banner of "we'll iterate," which is a delivery failure dressed up as product thinking. Eric Ries's original definition centres on learning, not launching: an MVP is an experiment, not a release. QA engineers often recoil at MVPs because the scope looks under-tested. The reframe: an MVP is tested deeply for the one assumption it exists to validate, not for completeness. Coverage is deliberately narrow and intentional — a different discipline from full-release testing, not an absence of discipline.
// Related terms
Problem validation
Problem validation is the practice of confirming that a problem is real, significant, and widespread before building a solution. The tool set includes user interviews (direct evidence), support ticket analysis (indirect signal), usage data (quantitative), and competitor research (market signal). Validation fails in two directions: false positive (you believe the problem is real because your most vocal customers have it, but most users don't) and false negative (you discount a real problem because it is hard to articulate in interviews). The lean loop — problem hypothesis → smallest experiment → decision — applies directly. For QA engineers moving into product, the "what could go wrong" instinct maps cleanly onto validation: both disciplines search for failure modes in a hypothesis. The shift is running that search before code exists, on assumptions rather than implementations.
Product-market fit
Product-market fit is the degree to which a product satisfies strong market demand — a state where users acquire, retain, and refer at rates that sustain growth without heavy marketing spend. Marc Andreessen defined it as "being in a good market with a product that can satisfy that market." Sean Ellis's heuristic: if more than 40% of surveyed users would be "very disappointed" if the product disappeared, PMF is likely. Before PMF, optimise for learning, not efficiency — every process and metric should target understanding what the market actually wants. After PMF, optimise for growth. For QA engineers pivoting to product, PMF reframes the entire quality conversation: before PMF you are validating the concept, not production polish; after PMF, reliability and quality become competitive differentiators that directly affect retention.
Product roadmap
A product roadmap communicates where a product is going and roughly when — it is a strategic communication tool, not a delivery schedule. Good roadmaps operate at the outcome level ("reduce time-to-first-value for new users"), not the feature level ("add onboarding wizard"), because specific features are hypotheses that may change; outcomes are the commitment. Common formats include Now/Next/Later (no dates, emphasises learning), quarterly OKR-aligned (dates, suits larger organisations), and opportunity-solution tree (connects problems to solutions visually). The hardest PM skill is saying no to stakeholders whose requests don't fit the roadmap and holding that line. QA engineers entering product already understand that not everything can be tested deeply before release; roadmaps require the same triage instinct applied to what gets built, not just what gets verified.
North Star metric
A North Star metric is the single number that best captures the core value a product delivers to customers — the one that, if it grows sustainably, predicts long-term business health. Spotify's was time-in-app; Airbnb's was nights booked. The North Star sits above vanity metrics (page views, sign-ups) and below revenue — it measures the value exchange, not the financial outcome. Choosing the wrong metric drives the wrong behaviour at scale: an engagement metric optimised without guardrails can increase usage while eroding user wellbeing. Supplement the North Star with counter-metrics to prevent gaming. For testers becoming PMs, this is a genuine mindset shift: QA thinks pass/fail (binary, per-release, reversible), while product thinks directional (continuous, long-horizon, probabilistic). Learning to act confidently on a trend rather than a verdict is the core adjustment.