How to create a prototype: 8 examples for product managers

Explore the different types of software prototypes and when to use each

Last updated: March 2026

Product teams create software prototypes to explore, validate, and test ideas for new experiences before committing to development. This guide covers different types of prototypes by fidelity and purpose — plus, how AI prototyping tools help product managers start building sooner.

"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. Product managers no longer need to wait for design or development resources because you can generate realistic flows and interfaces yourself in minutes. (As UX and engineering also adopt these tools, expect even more prototypes coming your way for review.)

When anyone can prototype, it helps the whole product team align faster on what to build and how — from minor UI improvements to major new experiences. But because they are effective for a variety of use cases, prototypes can also create ambiguity about purpose or polish. This can lead to confusion and rework, especially as more people start building them.

This guide explains key kinds of prototypes and examples of when to use each so you can align on expectations every time someone says, "Let's prototype this." Keep reading or skip ahead here:

What is a software prototype?
Types of software prototypes
How to create a software prototype
What are AI prototyping tools?

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.

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

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Types 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 explore several questions. This framing is more practical than specific terminology because it keeps your team aligned on effort and intent for each prototype.

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, align on direction, 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.

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

Try using low-fidelity prototypes for early ideation, mid-fidelity for UX exploration, and high-fidelity for customer-facing demos and engineering needs. There will be exceptions — so be clear with your team about the goal and audience before you start prototyping.

What about the flip side? When is a prototype not the right tool? It may 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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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 process is similar no matter which tool you use, but these steps are geared toward using AI to prototype:

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.

2. Choose the fidelity

When possible, start simple — include only the level of detail needed to explore 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.

4. Create it

Share the context you gathered with your AI prototyping tool, then start with a simple prompt such as, "Create a prototype of this feature." When it produces an initial version, keep chatting with AI to refine your prototype in small steps. Ask for targeted changes to the layout and flow until the prototype reflects what you need.

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. In all cases, be explicit about what you want input on so the feedback stays focused and actionable.

6. 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 exploring a different approach entirely. Repeat steps five and six 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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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 with working code to test and demo to customers in Aha! Builder

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 and product prototypes

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

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

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?

How to create a prototype: 8 examples for product managers

Explore the different types of software prototypes and when to use each

What is a software prototype?