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Build a proof of concept (PoC) first if you still need to establish that a critical technical or operational assumption can work. Build a minimum viable product (MVP) when feasibility is sufficiently established and you need to learn whether real users get enough value from a working product. If the open question is whether people understand or can use the experience, start with a prototype.
These are different experiments, not mandatory stages in a universal sequence. Choose the smallest one that can produce evidence relevant to your next decision.
Which should you build first?
Start by naming the biggest unresolved risk. The label matters less than the question the experiment is designed to answer.
| Unresolved question | Best first experiment | What to observe | Decision it can inform |
|---|---|---|---|
| Can the technology, integration, or operating approach meet a defined constraint? | A narrowly scoped PoC | A measurable feasibility result under stated conditions | Continue, change the approach, or stop before committing to product scope |
| Will users understand the idea or complete the intended workflow? | A prototype, from sketches to an interactive mock-up as needed | Where people understand, hesitate, or fail in the flow | Refine the experience or proceed to an MVP test |
| Will real users get enough value to adopt, return, or pay, with feasibility reasonably established? | A narrowly scoped MVP | Core journey completion and relevant product signals | Iterate, revise the hypothesis, or expand only when evidence supports it |
This is a practical decision framework, not a formal standard. The sources do not establish a universal order or numeric go/no-go threshold.
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What each artifact is meant to prove
Proof of concept: can the critical assumption work?
A PoC tests a bounded feasibility assumption. It might check whether an integration can exchange required data or whether a technical approach can meet a stated constraint. Its useful output is evidence for a decision; the code may be temporary and need not be suitable for production.
Bruno Fernando Antognolli and Fabio Petrillo propose this software-specific definition in their 2026 preprint: “A Proof of Concept (PoC) is a short-term experiment that validates hypotheses within a limited scope by assessing feasibility, mitigating risks, and supporting learning and decision-making.” This is the authors’ proposed framing, not a binding industry standard. The terminology varies across practice.
Prototype: can people understand or use the experience?
A prototype represents a concept, design, or workflow so a team can explore how it is understood or used before committing to a real product. It can be a sketch, a clickable mock-up, or another representation suited to the question. Microsoft notes that prototypes are often rough and may not function. Because the term is used loosely—and can overlap with PoC, pilot, or MVP—state what the prototype is testing.
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MVP: does a working product deliver value?
An MVP is a focused, working product used with real people and real data to learn about value and demand. Microsoft for Startups describes an MVP as “the earliest version of a product that delivers real value, supports real users, and generates real data.” Its guide emphasizes real infrastructure and actual use. That makes an MVP more than a mock-up or a curated demonstration.
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Microsoft also frames an MVP as a path toward revenue; treat that as the publisher’s position rather than a universal definition. A demo can communicate a concept, but a controlled presentation—especially one using curated data—does not by itself show how the product operates under real conditions.
How to design an experiment that changes a decision
For a PoC, define the boundary and evidence first
- State one important assumption. Make it specific enough to test, such as whether a named integration can meet a particular requirement.
- Set the conditions. Record the constraints and circumstances under which the result would matter.
- Choose observable evidence. Decide what result would support or refute the assumption before running the experiment.
- Record the outcome and decision. Capture assumptions, constraints, findings, and what the team chose to do next.
Antognolli and Petrillo’s 2026 preprint proposes planning, execution, and decision-making phases, and argues that a PoC should leave traceable architectural knowledge. Their systematic review reports that, under their stated criteria, they examined 20 practitioner sources and found zero of 172 retrieved academic documents with detailed PoC process descriptions. Those counts describe the authors’ review and inclusion criteria; they are not a census of all PoC practice.
For an MVP, connect measures to the product hypothesis
Choose signals that reflect the value you expect users to receive, rather than measuring everything. Microsoft lists these examples:
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Activation: whether users reach the core value of the product.
- Retention: whether they return.
- Conversion: whether they move toward a paid relationship.
- Time to value: how quickly the benefit appears.
- Reliability: how the system performs operationally.
These are candidate measures, not universal pass thresholds. The right signal depends on the product hypothesis; the source does not prescribe a general number that makes an MVP successful.
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What the evidence can—and cannot—tell you
A 2018 study by Dron Khanna, Anh Nguyen-Duc, and Xiaofeng Wang examined two software startups. In those cases, the relationship between business hypotheses and MVPs was incomplete and non-linear, and the entrepreneurs learned through hypothesis testing. The study was inspired by Lean Startup, but two cases do not establish a general outcome rate or prove that every MVP validates every business assumption.
More broadly, neither an MVP nor a PoC guarantees a correct decision. An experiment is only as useful as the assumption it targets, the conditions it tests, and the evidence collected. Keep the conclusion proportionate to what was actually observed.
Choose the smallest experiment that resolves the next uncertainty
If feasibility is the blocker, test it with a PoC before taking on product scope. If the experience is the uncertainty, use a prototype. Once feasibility is sufficiently established and the decision depends on real-world product value, build a focused MVP and observe how people use it. Document what each experiment can and cannot establish so a disposable test does not become an accidental production commitment.
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