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Build a proof of concept (PoC) first if your biggest unanswered question is whether a critical technical or operational assumption can work. Build an MVP when feasibility is reasonably established and you need to learn whether real users get enough value from a working product. If the uncertainty is about whether people understand or can complete a workflow, test it with a prototype. These are choices of experiment, not mandatory stages in a universal build sequence.
Choose by the question you need answered
Start with the risk that could change your next decision. Pick the smallest artifact that can produce useful evidence about it.
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| Main unresolved question | Best first experiment | What to observe | Decision it supports |
|---|---|---|---|
| Can the technology, integration, or operating approach meet a defined constraint? | A narrow 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 appropriate | Where users 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 activation, retention, conversion, time-to-value, or reliability signals | Iterate, change the hypothesis, or expand only when evidence warrants it. |
This is a practical risk-based framework, not a formal standard. The sources do not establish a universal sequence or numeric go/no-go threshold.
What a PoC, prototype, and MVP are for
Proof of concept: test feasibility
A PoC tests a bounded assumption, such as whether an integration can meet a latency constraint or whether a data-processing approach can handle a defined input. Its useful output is evidence for a decision; the code may be disposable and need not be suitable for production.
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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.” They describe PoCs as short-term, hypothesis-driven, and decision-oriented. This is the authors’ proposed framing, not a binding industry standard; terminology and practice vary. Read the paper on arXiv.
Prototype: test the experience
A prototype represents a concept, design, or workflow so you can investigate how people respond before committing to a real product. It may be a sketch, 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 inconsistently and can overlap with PoC, pilot, or MVP, explain what the prototype is testing rather than relying on its label.
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MVP: test product value in use
An MVP is a working, focused product that lets real people use the core offering and generates evidence about its value. Microsoft for Startups defines it as “the earliest version of a product that delivers real value, supports real users, and generates real data.” That is Microsoft’s guidance; the key distinction for this decision is actual use, not a polished demonstration or mock-up. Microsoft for Startups’ MVP guide.
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A controlled demo can explain an idea, often with curated data, but does not by itself show that the product works under real conditions or that users will return to it. A convincing presentation is not equivalent to evidence from real use.
How to design a useful PoC
- Write down the assumption. State the technical or operational claim that would put the product plan at risk if it proved false.
- Set the boundary and conditions. Specify the input, environment, integration, constraint, and result that matter. Keep the experiment narrow enough that its outcome answers the stated question.
- Define evidence before running it. Decide what result would support the assumption, what would refute it, and what uncertainty would remain. No universal threshold applies; the relevant measure depends on the constraint.
- Record what happened. Capture assumptions, constraints, method, findings, and limitations so later decisions do not rely on an unexplained prototype or recollection.
- Make the decision explicit. Continue, revise the approach, investigate a remaining uncertainty, or stop. Treat any code reuse as a separate engineering decision rather than assuming experimental code is production-ready.
Antognolli and Petrillo’s 2026 paper proposes planning, execution, and decision-making phases and argues that PoCs should leave traceable architectural knowledge. In their review, using their stated methodology and inclusion criteria, they examined 20 practitioner sources and found that none of 172 retrieved academic documents met their criteria for detailed PoC process descriptions. Those counts describe the authors’ review, not a census of all PoC work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an MVP
Connect each measure to a product hypothesis and the intended user journey. Microsoft lists these as possible signals, not universal pass thresholds:
- 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 intended benefit appears.
- System reliability: whether the product operates reliably in use.
Choose only measures that help answer the decision at hand. A product focused on a one-time task may need different evidence from one whose value depends on repeated use. The sources do not prescribe a universal user count, duration, budget, or success cutoff.
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 authors say the work was inspired by Lean Startup. This small case study does not establish a general outcome rate or prove that every MVP validates every business assumption. Read the study on arXiv.
Quick Recap
Common sequencing mistakes
- Building an MVP to answer a feasibility question: A broad working product can commit time and scope before a critical technical risk is resolved. Isolate that risk in a PoC first when it could invalidate the plan.
- Calling a mock-up an MVP: A mock-up can test comprehension or flow, but it does not provide evidence from users operating a working product.
- Treating a PoC as a product foundation by default: Experimental code is optimized to answer a question, not necessarily to meet production needs. Assess reuse deliberately.
- Following a fixed PoC-to-prototype-to-MVP ladder: If the main uncertainty is already about user value and technical feasibility is sufficiently understood, an MVP may be the right first experiment. If the issue is workflow comprehension, a prototype may be enough.
- Using labels without stating the test: Since usage varies, name the hypothesis, evidence, and decision rather than assuming everyone means the same thing by “PoC” or “MVP.”
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