How to create a prototype: 8 examples for product managers

A practical guide to software prototyping: The different kinds of prototyping, when to use each, and how to build one with AI

Last updated: August 2026

Product teams use software prototyping to explore, validate, and test ideas for new experiences before committing to development. Product prototyping can start early — while you are still deciding what a feature should do — and continue as you figure out the design and technical details. This guide covers different kinds of prototypes by fidelity and purpose, who should create them, and how to use prototypes throughout product development.

"Let's mock it up real quick." "Can you make this a clickable flow?" "We need a demo version to show customers." Product teams often use various shorthand to talk about visualizing ideas. These requests sound different, but they are really pointing to the same need: a prototype.

Software prototypes have always been essential to exploring and testing new product ideas. And AI prototyping tools now make it simple for anyone to create them quickly. That opens up prototyping to more people on the product team and creates a lot more ways to use it.

This guide covers software prototyping from that perspective: what you are trying to figure out, what to create, who should be involved, and how to bring what you learn back into the product plan. Keep reading or skip ahead here:

What is software prototyping?
What is a software prototype?
Why should product teams prototype before building?
Kinds of software prototypes
How to choose the right prototype for your audience
Who should own prototyping?
How to create a software prototype
How do you turn a product requirement into a working prototype?
How do you connect what you learn from a prototype back to your roadmap?
What are AI prototyping tools?

What is software prototyping?

Software prototyping is the practice of creating and testing representations of a product before committing to development. Teams use prototypes to answer questions about a proposed product or experience.

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What is a software prototype?

A software prototype is a visual, interactive representation of a digital product feature or experience. It shows how the new functionality will look and behave — helping product teams explore options, validate the direction, and iterate toward a version that you will release for customers.

Even though people often use the terms interchangeably, interactivity differentiates prototypes from other design artifacts (like sketches, mockups, and wireframes).

Mockups and wireframes are usually static. There is some overlap, though. For example, a clickable wireframe on a whiteboard can function as an early prototype if you use it to walk through flows and gather feedback.

You might also hear the term rapid prototyping. This usually refers to physical products that are produced quickly via 3D printing or other additive manufacturing methods. The process is different from creating software prototypes, but the goal is similar: create something tangible as quickly as possible to test ideas and inform the final product.

A prototype created in Aha! Roadmaps for user testing

An example of a software prototype created in Aha! Roadmaps that is ready for quick feedback

Related:

Why should product teams prototype before building?

A prototype gives you something that a written requirement cannot: a chance to try the experience before anyone builds it.

Maybe the new workflow sounded simple when you described it in a feature. Once you can click through it, you notice that an important action is buried. Or you show it to a customer, and they head in a direction you did not anticipate. Even a rough prototype can surface questions that are hard to see when the idea exists mostly in words.

For product managers, that is the value of product prototyping early in the process. You can try a few approaches, get reactions from customers and teammates, and make changes while you are still deciding how the feature should work.

You might not need a prototype for every feature. But when you are still deciding how something should function, it is much easier to change the prototype than the code base.

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Kinds of software prototypes

For product teams, the best way to categorize software prototypes is by fidelity and purpose.

Fidelity: How close it is to the real product experience
Purpose: Which questions it helps you answer

Think of these dimensions as a matrix. You might create prototypes at different fidelity levels for the same purpose or use a similar style of low-fidelity prototype to investigate several questions. This framing is more practical than specific terminology because it keeps your team aligned on effort and intent for each prototype.

You will also need to keep in mind who your prototype is for — your core product team, cross-functional stakeholders and leadership, or end users. Different kinds of prototypes suit certain audiences, although some can work across groups. Your audience will influence the fidelity you choose and the prototyping tools you use.

Now, let's break things down.

Software prototypes by fidelity

Fidelity level	What it is	How to use it
Low-fidelity prototype	Rough, lightweight representation — captures structure and flow, but not visual detail	Explore and validate early concepts, compare options, and communicate ideas before investing in design. You can create lo-fi prototypes on whiteboards in Aha! software.
Mid-fidelity prototype	Structured but unrefined design with some UI/UX elements and limited styling	Run usability tests and gather feedback on layout, navigation, and key interactions before committing to high-fidelity design or development. You can generate and embed mid-fi prototypes in Aha! software.
High-fidelity prototype	Robust and fully interactive — closely matches the final product's design and functionality	Secure stakeholder buy-in, demo for users, and refine toward a Minimum Lovable Product for launch. (Or in some cases, create a database-backed prototype for direct use by customers.) You can build high-fi prototypes and business applications in Aha! software.

A lo-fi prototype created in Aha! Whiteboards for easy feedback

An example of a lo-fi prototype created on a whiteboard in Aha! software

Software prototypes by purpose

Purpose	Core questions	Typical fidelity	When to use it
Discovery	Is there a problem here worth solving? Do users care?	Low	When you are early in customer research and ideation, before committing to a solution
Usability	Does this flow make sense? Can users complete the intended task?	Any (often mid)	When you need input on structure and flow to improve UX design
Feasibility	Can we build this? Does the approach work within our constraints?	Any	When evaluating if the technical direction is realistic before you commit meaningful engineering effort
Viability	Will customers adopt or pay for this? Does it deliver enough value?	Mid-high	When gathering feedback from customers to confirm demand, pricing, and that the solution meets their needs
Integration and performance	How does this fit with what exists? Is it fast and reliable?	High	When testing how the product behaves with an actual database and system infrastructure

What are the 4 kinds of prototyping?

There is one other way you will see prototypes classified. The four traditional approaches — throwaway, evolutionary, incremental, and spiral — describe what happens to the prototype as the work progresses:

Throwaway prototyping: Build something to answer a specific question, then discard it once you have the answer.
Evolutionary prototyping: Keep refining the same prototype as the requirements become more detailed.
Incremental prototyping: Prototype separate parts of a larger system, then bring them together as the overall product develops.
Spiral prototyping: Build successive prototypes while evaluating requirements and risks at each cycle. (This is generally used for larger, more complex software projects.)

How to choose the right prototype for your audience

Some types of prototypes have clear audiences. For example, you would show a high-fidelity integration prototype to an engineer or other technical leader who can provide the right feedback. When you are not sure what type of prototype to share, use this high-level guidance:

For your core product team, start with low-fidelity prototypes that you can create quickly to explore concepts, agree on direction, and evaluate feasibility fast. Move to mid-fidelity when you need more detailed feedback on user flows, and high fidelity for technical review.
For cross-functional stakeholders and leadership, use mid- to high-fidelity prototypes so you can communicate value clearly and build confidence in the planned solution once your core team has aligned on the approach.
For end users, choose mid-fidelity prototypes when you are testing usability and design. Use high-fidelity prototypes when you need feedback that reflects actual product use, so you can validate that the solution you are building meets user needs and works as intended.

What about the flip side? When is a prototype not the right tool? It might make sense to skip the prototype step when:

You do not have a clear question or decision that the prototype should inform.
The problem is already well understood, so existing design and development frameworks provide enough guidance.
The change is minimal and does not introduce new user workflows or interfaces that need further exploration.
The question is primarily about visual design or style, and a wireframe or mockup can provide the same insight.

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Who should own prototyping on a product team: PMs, UX designers, or engineers?

Product managers do not need to become UX designers to create prototypes. You can make a prototype without designing the final experience.

Imagine building a prototype to see how a feature could work, testing it with customers, and giving the team something concrete to discuss. As the idea develops, UX designers can apply their expertise to the interaction and visual design, while engineers can weigh in on technical feasibility. That means ownership can change over the course of the work. The person with the question can start the prototype, then bring in the expertise the idea needs from there.

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How to create a software prototype

You can still prototype in design tools or manually code them. But AI prototyping works faster and takes less effort — making it an increasingly popular choice (you can learn more about this in the next section).

The product prototyping process is similar no matter which tool you use, but these steps are geared toward using AI:

1. Clarify the purpose

Write down the top questions you need to answer with your prototype. Make them specific to your product (for example, "Can account admins easily add new users?"). Note who you will share it with — teammates, leadership, or customers — and what decisions you want to make once you have their feedback. This will shape the input you give to your AI prototyping tool. Use those answers to guide your prompt.

2. Choose the fidelity

When possible, start simple — include only the level of detail needed to answer your questions. You can always increase fidelity as you learn more, but it is harder to walk back something that feels too polished. Extra detail might also distract people from the core problem you are trying to solve.

3. Gather product context

The context available to inform your prototype will vary depending on where you are in the product development process. A discovery prototype might start with only a few ideas and customer requests, while a viability prototype could draw from full studies and detailed feature descriptions. You can always add more — like personas or brand style guidelines — to help AI produce an accurate result.

If you are updating an existing experience, include screenshots of the current product so AI has a visual reference, too.

4. Create it

Share the context you gathered with your AI prototyping tool and describe what you want it to create. If you are updating an existing experience, explain what should change and what should stay the same.

Review the overall structure and flow before adjusting individual controls or visual details. Once those are right, get more specific.

Get your prototype in front of the people you designed it for. Share it in a place where they can easily interact with it and comment. Depending on the type of prototype, this step might only need a quick review with your product team — or it could involve structured user testing.

6. Collect feedback

Be explicit about what you need input on so feedback stays focused. As people interact with your prototype, ask pointed questions such as “Where did you hesitate?” or “What felt confusing?” Then capture what you hear and see. For internal teams working on a lightweight prototype, comments and screen recordings are often enough. For customer-facing research, you might lead more structured sessions with robust prototypes — so you can gather and analyze feedback at scale in your discovery tool.

7. Iterate it

Use the feedback along with your own observations to guide your next version. Ask your AI prototyping tool to help summarize input and apply changes, whether that means clarifying a confusing step or trying a different approach entirely. Repeat steps five, six, and seven until you feel confident you can answer the questions you outlined at the start.

To see a step-by-step walkthrough of how to generate prototypes with AI, check out the tutorial below:

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How do you turn a product requirement into a working prototype?

If you already have a detailed product requirement, you have much of the context you need for a first prompt. Share the user, workflow, expected behavior, and any constraints with your AI prototyping tool. Then, generate a first version and see how the requirement translates into an interactive experience.

Click through the prototype with the requirement beside you. Ask yourself: What happens after this action? Which options appear here? What does the empty state show?

Make those decisions in the prototype, but do not leave them there. If an answer changes what engineering needs to build — a new state, a default behavior, another path through the workflow — add it to the requirement too.

In Aha! Roadmaps, you can do this without moving the work somewhere else. Generate a prototype from a feature description, embed it directly on the feature, and update the requirement as you revise the proposed experience.

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How do you connect what you learn from a prototype back to your roadmap?

The prototype could change several times. The feature should change with it. If you add a step, drop part of the workflow, or uncover more work, update the requirements and estimates accordingly.

You might also find that the feature is larger than you estimated, that part of it can wait for a later release, or that another feature needs to come first. Update the roadmap when those discoveries affect the scope, timing, or sequence of the work.

Keep the prototype attached to the feature it represents. In Aha! Roadmaps, teammates can review and comment on an embedded prototype while the feature and requirements are updated alongside it. If you create another version, replace the old one so there is no guessing which prototype is current.

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What are AI prototyping tools?

AI prototyping tools use generative AI to turn product ideas and data into working prototypes in minutes. Instead of manually laying out or coding every screen, you simply describe what you want to build, who will use it, and how it should work for the AI tool. Then, it generates interactive layouts and sample data for you.

You can also tell AI how detailed the design should be and explain the purpose of the prototype so it can tailor the output. This helps product managers, UX designers, and engineers create and refine realistic prototypes much faster. The team can also spend more time deciding what to build next.

For example, Aha! software offers the flexibility to create prototypes in different ways with AI — from low- to high-fidelity. You can:

Create lightweight prototypes on whiteboards for early discovery in Aha! Whiteboards
Generate prototypes for product features and embed them for review, then quickly generate a new version in Aha! Roadmaps
Build robust prototypes backed by a database to test and demo to customers in Aha! Builder

Using Elle, the AI assistant in Aha! software, to build a prototype of a feature

Generate prototypes with AI based on a feature description in Aha! software.

AI prototyping is fundamentally shifting what product managers are able to accomplish. You have a vision, and you know what users need. You can now turn your ideas into something tangible — and start bringing solutions to life sooner.

FAQs about software prototyping and product prototypes

What is the difference between a prototype and a Minimum Lovable Product?

How long should it take to create a prototype for a new feature?

How do you know when a prototype is good enough?

What are the most common mistakes teams make when prototyping?