AI Prototyping Tools 2026: Magic Patterns, Figma Make, v0, Lovable, Google Stitch, Claude Design and Bolt (Compared)

For a 3-screen onboarding prototype, shortlist Magic Patterns for a dedicated prototype workflow, Figma Make when your starting assets live in Figma, and v0 when the next owner wants code in GitHub. The best AI prototyping tools for your project are the ones that preserve the context you already have and let a participant complete the task you need to observe.

Pricing and documentation verified on 23 October 2026. This is a comparison of documented workflows, with a reproducible onboarding brief. Authenticated generation was unavailable during this review, so there are no measured completion times, generated test outputs or observed repair counts. The recommendations below describe fit, not a hands-on speed ranking.

AI prototyping tools at a glance

Start with the source material you already own. A designer with approved Figma frames has a different starting point from a founder with a paragraph, or an engineer with an existing component package.

Starting prices below are for one person entering a paid plan, except where free access is the relevant starting point. All prices are in US dollars. Figma's quoted price requires annual billing; the other paid entries use monthly billing. Free plans are ongoing limited access, not time-limited trials. Google publishes Stitch's cost statement in its own product FAQ rather than on a separate pricing page.

How these were picked

A useful prototype lets you learn something before committing engineering time. The selection therefore turns on four criteria: what you can bring in, which interactions the prototype needs, how existing design rules enter the workflow, and what the next person receives.

A design system is the reusable set of components and rules behind your product: its buttons, inputs, colors, spacing and behavior. Matching a screenshot's appearance is different from using those components. A stateful prototype remembers something the participant did, such as their selected goal, while they move through the flow.

The seven candidates were assessed against their makers' current documentation and pricing information. Their public entry points were also opened. That access check did not become a generation test: Magic Patterns and v0 reached sign-in, Lovable and Claude presented browser verification, Stitch did not load a usable editor, and the Figma Make and Bolt public composers did not yield authenticated workspaces or generated artifacts. These are limitations of this review environment, not evidence that the products fail to generate prototypes.

Consequently, what came out of the planned shared generation test, and what needed fixing, remain unmeasured for all seven tools. No recommendation below rests on invented first-pass quality, stopwatch results or usability findings. The shared prompt and repair checkpoints make the missing experiment explicit and reproducible.

AI powered prototyping tools versus app builders

For this decision, the deliverable is a testable experience. It can simulate a successful submission without creating an account, storing a customer record or sending an email. That is often sufficient to ask whether someone understands the next action.

A production app has additional responsibilities. If your next milestone is running a business on the generated software, use the separate AI app builders comparison and vibe coding tools guide. Here, adding a backend is justified only when it is necessary to answer the research question.

For example, testing whether people understand an onboarding goal selector needs working choices and navigation. Testing whether an invitation reaches a colleague needs a different prototype and a different brief. Keep those experiments separate so an impressive technical feature does not substitute for the decision you meant to make.

AI prototype generator: one shared onboarding brief

Use the same starting prompt and acceptance checks in every candidate. Otherwise, a tool given a precise state model is being compared with one asked merely to make an attractive onboarding page.

The following is the exact brief prepared for this comparison. Northstar is a fictional product; the dimensions are proposed design constraints, not measurements of a generated result.

Create a clickable three-screen onboarding prototype for a fictional team-planning product called Northstar. Screen 1: Welcome, required full name and work email, and Continue. Screen 2: Choose a goal from Plan a project, Coordinate a team, or Explore; exactly one choice is required. Include Back and Continue. Screen 3: Review the entered name, email and selected goal, with Back, Finish and Start over. Finish shows a confirmation inside screen 3, without adding another screen. Retain values when going Back. Invalid email and missing required fields show inline errors. Start over clears all fields. Use mock data and local state only: no authentication service, database, email sending, payments or external API. Use a white background, dark text, indigo primary buttons, an 8px spacing scale, 12px corner radius, clearly labeled inputs, visible keyboard focus and responsive desktop/mobile layouts. Do not add extra screens or features.

This brief separates appearance from behavior. A clickable image can illustrate the sequence, but it does not demonstrate that a participant can enter their own name, change a goal and see the revised value in the review screen.

Use these acceptance checks before inviting someone to a session:

• Navigation: Continue advances to the intended screen; Back returns without discarding valid entries.
• Validation: Empty required fields and an invalid email produce an understandable inline error.
• Selection: Exactly one goal can be active, and its selected state is visible.
• Review: The summary shows the participant's entries, rather than fixed example text.
• Completion: Finish confirms inside Review. Start over clears the prior participant's data.
• Input: Labels remain visible, keyboard focus can be followed, and the layout works at the intended viewing size.

The meaningful before-and-after is the transition from an attractive draft to a flow that passes those checks. Because no generated draft was obtained here, the examples below are conditional repair instructions, not reported defects in any product.

If Back loses the name, the repair is: “Keep all onboarding values in shared local state when navigating between screens.” If Review displays a sample goal, ask it to render the current selection. If Finish adds a dashboard, remove that extra screen and show the confirmation in place. Change the smallest behavior that fails, then repeat the affected task.

Keep design-system evaluation as a separate pass. First use the identical plain brief. Then provide the same approved reference and component materials to each tool that supports them, recording any required conversion or plan upgrade. Giving one candidate a complete component package and another a screenshot would not establish which respects a system better.

The best AI prototyping tools by workflow

1. Magic Patterns: start here for a dedicated prototype workflow

Magic Patterns is an AI design workspace for turning a requirement or screenshot into an interactive product prototype. It belongs first on the shortlist when the immediate deliverable is a product discussion or research session, particularly when you want to explore alternatives without making the prototype a production-app commitment. Its documentation connects that workflow to reusable design systems and engineering handoff. That is a reason to try it first, not evidence that it completed this brief fastest. Magic Patterns introduction.

Best for: A product manager or designer preparing a bounded feature demonstration.
Standout: A design workspace with reusable components and rules, plus code and Figma handoff options.
Pricing: Starter starts at $20 per seat/month, billed monthly. Maker pricing.
Free trial: A limited Free plan is available; a completed prototype is not a guaranteed unit of its allowance.

Apply the shared brief. Start with Northstar's entire flow rather than generating unrelated screens and trying to reconcile them later. The review screen is the important checkpoint: its content must come from the earlier form and selection. An attractive goal selector is only part of the task.

For an established product, attach system context before evaluating visual fidelity. Magic Patterns documents creating a system from scratch or seeding one from GitHub, an npm package, Figma or a website. Its system includes components, colors, typography and rules. This gives you more to inspect than whether the primary button happens to be indigo. Design-system documentation.

Ask the reviewer to identify the input and button components used in the prototype. If the result only resembles the approved controls, record it as visual matching. If it uses the actual reusable components, record that separately. The distinction matters when the engineer receiving the work needs the same disabled, error and focus states as the existing product.

The plan boundary matters. The pricing page includes export options such as GitHub, MCP and Figma in Starter, but lists custom design-system import under Enterprise. MCP, Model Context Protocol, is a way for an AI coding tool to retrieve context or use another tool's capabilities. Do not interpret a general feature page as proof that every custom import route is included in the entry subscription.

Conditional repair checkpoint: If an imported reference produces a close-looking form with the wrong error treatment, point to the approved input component and its error variant. Ask for that substitution while preserving navigation and data. This is more precise than asking the tool to “make it more on brand.”

Handoff judgment: Choose Magic Patterns when exploration is the main job and you want several routes out. Before treating an export as a head start for engineering, have the next owner open it and confirm component identities and the onboarding state model. A shareable preview and reusable code solve different parts of the handoff.

A reproducible starting recipe for Magic Patterns:

1. 1

   Set the scope before prompting

   Use the Northstar brief unchanged for the plain comparison. Keep a copy of it alongside the acceptance checks, and leave out production integrations.

2. 2

   Add the system in a separate fidelity pass

   For the system pass, open Design Systems from the dashboard and create or select the relevant system. Add the approved components, colors and rules using the documented import route available to your account.

3. 3

   Inspect behavior before polishing

   Enter a name and email, choose a goal, return to the earlier screen, and check the review summary. Record each failure before requesting a targeted correction.

4. 4

   Check the destination with its next owner

   Open the shared preview as the intended reviewer. Then use the required Figma or code handoff route and confirm that the next person can continue the work.

2. Figma Make: choose it when the existing design context lives in Figma

Figma Make is Figma's prompt-to-app tool for turning ideas and existing designs into interactive experiences. It is the natural first candidate when a designer already has approved frames and the team wants the prototype conversation close to those assets. Make can take existing designs and components as context, share previews and publish a dedicated URL. Explore Figma Make.

Best for: Designers and product managers whose approved interface assets are already in Figma.
Standout: Make kits combine code packages, library styles and written usage rules.
Pricing: A Professional Full seat starts at $16/month, billed annually. Maker pricing.
Free trial: Starter offers limited access; Make kits require a Full seat on a paid plan.

Apply the shared brief. Use the same text for the initial generation. In the separate fidelity pass, attach the approved onboarding frames and select the relevant Make kit through Add context. The frame answers what the interface should look like. The brief still needs to explain what happens after a participant changes a choice or enters an invalid address.

A Make kit packages npm code modules, variables and styles from published libraries, and guidelines describing how to use them. An npm package is a distributable bundle of code, such as your actual input and button components. That gives the system pass a meaningful question: did the prototype use the intended building blocks and states? Make kit documentation.

Figma also names a limitation worth checking before promising exact fidelity. Extracted library styling uses a subset of variables and raw CSS values; it does not preserve every original variable expression. CSS is the styling code that determines appearance. A matching default button therefore does not prove that all themed or conditional states have carried through.

For Northstar, inspect the selected goal, Continue action, invalid-email error and keyboard focus. Those are small surfaces where an apparently faithful design can still differ from the product's rules. Add missing state instructions to the kit's guidelines so the context explains behavior as well as color.

Conditional repair checkpoint: If the form uses the right layout but an improvised error message style, reference the kit's input rule and change that state only. Do not ask for a whole-page redesign when the research question depends on whether the error can be understood and corrected.

Handoff judgment: Figma Make fits a workflow where design review remains central. Its product page describes code-backed, visually editable work and copying content to the Figma canvas. Decide which artifact engineering will receive and open that actual artifact together. The same page marks working on a local codebase as “Coming soon,” so that promise is not a basis for buying an available workflow today. Figma Make product page.

The Figma Make review covers its broader workflow. For this narrower choice, the reason to pick it is the context your team already maintains, followed by the ability to check the prototype against it.

3. v0: choose it when the next owner wants a code repository

v0 is Vercel's AI app-building environment, with documented design imports and GitHub workflows. It is the strongest fit on this shortlist when the prototype is an early implementation that engineers intend to inspect and continue. Its GitHub integration keeps code in a repository and works through branches and pull requests, giving the handoff a concrete destination. v0 GitHub documentation.

Best for: A founder or designer collaborating with an engineer who wants to continue the prototype in code.
Standout: Multi-frame Figma context, reusable design-system setup and repository handoff.
Pricing: Plus starts at $30 per user/month. Maker pricing.
Free trial: A Free plan is available, with a daily message limit.

Apply the shared brief. Ask for the bounded onboarding flow with local state. Keeping data local means the browser can remember values during the session without setting up a database. The engineer should be able to trace where the name, email and goal are stored, and how Back, Finish and Start over affect them.

For the fidelity pass, v0's current Figma integration can read multiple frames and use their layout, text, tokens and assets to build a flow. The integration requires a paid v0 plan. A screenshot can communicate appearance; a connected design file supplies additional structure and context. v0 Figma documentation.

The current system workflow is Design Systems 2.0. It saves a reusable skill describing the real components, properties, tokens and setup that v0 should use. You can supply source repositories, an installable package, an app that already uses the package, Figma references and documentation. v0 builds a starter for review before saving the system. Design Systems 2.0 documentation.

That starter review deserves attention. A component package may require a theme provider, fonts or global styles before its controls look correct. An example app shows how those pieces work together. If the starter is wrong, adding the full onboarding flow makes the diagnosis harder rather than proving the tool is unsuitable.

Conditional repair checkpoint: If the generated goal choices use a substitute control, name the approved component and allowed variant. If values disappear on Back, correct the state model separately. This keeps a system-fidelity issue from being confused with a navigation issue.

Handoff judgment: v0's advantage here is the documented continuation path. Ask engineering to review the prototype branch, explain the fake data, and identify which parts are worth retaining. A repository makes that inspection possible; it does not certify that the generated code already meets your production standards.

For a team with no engineer involved in the next stage, the repository path may contribute little to the immediate user session. In that situation, choose on review and interaction needs first. The v0 alternatives for design to code comparison covers the broader handoff decision.

4. Lovable: choose it when the research question depends on working form state

Lovable is a conversational web-app builder with a documented path for design systems and code synchronization. It belongs on this shortlist when the prototype needs to behave like a small application: accepting input, remembering choices and showing a personalized review. Keep the brief narrow, because a product that can build an app still needs instructions about which app responsibilities the prototype should omit.

Best for: A founder or product manager who needs a functional web-flow simulation.
Standout: React design systems on paid plans, with explicit setup and adherence checks.
Pricing: Pro starts at $25/month for the entry workspace plan. Maker pricing.
Free trial: A Free plan is available, with limited build credits.

Apply the shared brief. Ask for Northstar's name and email inputs, a single selected goal and a review bound to those values. A participant changing “Explore” to “Plan a project” should see that change reflected in Review. That is the testable behavior; a connected database does not improve this particular question.

Lovable's current design systems are available on paid plans and work natively with React components, reusable pieces of a web interface. A design system lives in its own project. Connecting it copies its components and rules into the prototype project and brings along required dependencies. The documentation describes setup verification and checks for deviations such as raw colors or custom components. These are documented mechanisms, not a claim that this review observed perfect adherence. Lovable design-system documentation.

There is an important distinction for an established product team: wrapping an existing npm package as a design system is Enterprise-only. “Design systems included” on the entry paid plan does not mean every way of importing your current production library is included.

Updates also require a clear owner. When a connected project accepts a design-system update, local files belonging to that system are replaced. If a button's behavior needs a permanent change, make that change in the system rather than relying on an edit that the next update will overwrite.

Conditional repair checkpoint: If the form works but the selected goal looks like a bespoke card, request the approved selection component. If a system update overwrites a local styling correction, move the correction to the source system. Record those as separate kinds of rework: product logic versus shared component maintenance.

Handoff judgment: Lovable documents exporting and synchronizing project code with GitHub, including two-way synchronization. The receiving engineer should inspect that project with a short note saying which behavior is simulated and which component files come from the system. Lovable GitHub documentation.

A plain text-only demo may not need the extra system setup. An existing product with a mature library does. Choose the route based on the prototype's relationship to that product, rather than assuming that a matching screenshot establishes a maintainable implementation.

5. Google Stitch: choose it for connected screen exploration

Google Stitch is Google Labs' AI design canvas for turning prompts, sketches and screenshots into interface designs. Google documents related screens connected through interaction hotspots, plus Figma and code export. That makes it a candidate when the first research question concerns sequence, information and visual direction. It does not establish that a click-through export implements every form-state requirement in the Northstar brief. Stitch's official product FAQ.

Best for: Exploring screen directions and the order of an early user journey.
Standout: Multi-screen hotspot prototypes and portable design rules through DESIGN.md.
Pricing: Google says Stitch is currently free of charge in its official cost FAQ.
Free trial: Free access is subject to daily credits; it is not an unlimited trial promise.

Apply the shared brief. Begin with the same complete onboarding specification. Then distinguish a screen connection from working input. A hotspot can take a participant from Welcome to Goal. A stateful flow must also carry their entered name into Review, retain it on Back, and clear it on Start over.

That distinction should shape the session. If the research question is whether the goal choices make sense, a connected screen prototype may be enough. If the question is whether a person can correct a mistaken email and finish successfully, the form and its data need to work. Do not mark the latter as passed because the visual route is navigable.

For consistency, Google documents DESIGN.md, a portable file of design rules that can be imported and exported between projects. It explains the intended use of visual choices so another generation has more context than an isolated screenshot. This is useful system guidance, but a file describing rules is not by itself evidence that the output imports your organization's existing component package. Google's DESIGN.md announcement.

Conditional repair checkpoint: If the goal screen departs from the welcome screen's spacing or type treatment, supply the shared design rules and ask for consistency. If Review cannot display entered values, either complete that behavior in the code continuation or narrow the user session to what the prototype can demonstrate. Do not present a static summary as a working personalized one.

Handoff judgment: Google's product page explicitly lists static HTML export, alongside HTML/CSS, Tailwind support, Figma export and MCP connections. Static export is useful starting material, but the receiving engineer needs to check which interactions and state transitions are implemented in the exported artifact. Code that reproduces the appearance and code that reproduces the behavior are different acceptance criteria.

Stitch therefore earns a place for exploration without earning an unobserved speed or fidelity score. Its scope is a good fit when the next decision is about the screens. When the next decision depends on application behavior, prove that behavior before running the session.

6. Claude Design: choose it for a prototype that grows out of the conversation

Claude Design is Anthropic's visual creation capability inside Claude conversations, Artifacts and Claude Code. It can take reference material, support on-canvas editing and hand work to a coding workflow. It is a useful candidate when the prototype follows naturally from an existing product discussion and written requirements. Claude Design product page.

Best for: A product manager or founder turning a written discussion into a visual artifact.
Standout: Conversational iteration, on-canvas editing and code handoff within the Claude workflow.
Pricing: Claude Design is included on the $0 Free plan. Maker pricing.
Free trial: Free access uses the plan's shared usage limits.

Apply the shared brief. Provide the whole Northstar specification before requesting visual refinement. Keep the conversation explicit about the difference between an account-creation simulation and real account creation. The participant should be able to complete the task without a live authentication service or email delivery.

Anthropic documents attaching screenshots and codebase context, using design systems, and exporting or sharing the result. For the system pass, provide the same approved components and rules used elsewhere, where the account supports that setup. Free access to the Design capability should not be read as a promise that every organization-level system workflow or code feature has the same entitlement.

The review should focus on transitions as well as appearance. If the goal changes, does the summary change? If a reviewer edits text directly on the canvas, does it still express the same task and match the brief? Keep the acceptance checks outside the chat so that a plausible explanation from the model is not mistaken for a verified result.

Conditional repair checkpoint: If the review screen contains example values, specify that it must render the participant's current local state. If an edit changes the number of screens, restore the original flow before evaluating it. These are proposed checks, not problems observed in a Claude-generated output here.

Handoff judgment: Anthropic lists HTML export and handoff to Claude Code. Use the receiving engineer's actual destination as the final check. If the next owner instead needs a Figma-native artifact, evaluate that requirement separately rather than assuming an HTML export satisfies it. The Claude Design versus v0 comparison explores that choice further.

Design activity now draws on the same usage pool as other Claude work, rather than a separate weekly Design allowance. A long iteration session can therefore compete with other tasks on the account. Anthropic's getting-started guide. For the broader access question, see Claude Design's free availability.

7. Bolt: choose it when existing code is part of the experiment

Bolt is an AI app-building environment that can start from a prompt, an existing GitHub project or a Figma frame. It belongs in this comparison when the prototype is closely tied to code the team already has, or when an engineer will inspect and extend it immediately. For an isolated onboarding concept, those capabilities are reasons to consider it, not reasons to add unnecessary app infrastructure. Bolt introduction.

Best for: A prototype attached to an existing code project or an immediate engineering review.
Standout: Repository continuation and an explicit choice of Figma import routes.
Pricing: Pro starts at $25/month, billed monthly. Maker pricing.
Free trial: A Free plan is available with daily and monthly token limits.

Apply the shared brief. Keep the generated work limited to the local onboarding simulation. If starting from an existing project for the separate fidelity pass, make clear which components to reuse and which areas of the project should not change. The outcome to inspect is still the participant's task, rather than how much software the tool generated.

Bolt's Figma documentation provides a particularly useful distinction. Its import works with a single frame, not an entire Figma project. When using a design system, the docs instruct you to choose Screenshot rather than Code, because the Code route is not aware of that system. That is a concrete reason to choose an import mode deliberately. Bolt Figma import documentation.

For the Northstar flow, importing one frame does not supply the missing interaction rules for all the screens. Add the complete brief and the required transitions. The reference establishes a starting visual context; the instructions establish what the prototype must do.

The pricing page also separates individual prototyping from the dedicated system workflow. Its comparison table places private npm registries and Design System Knowledge in Teams rather than Free or Pro. An individual Pro subscription should therefore not be bought on the assumption that it includes those team features.

Conditional repair checkpoint: If a Figma-based generation substitutes components despite a configured system, check that the documented Screenshot route was used before rewriting the design prompt. If the right components are present but navigation is wrong, repair the state transitions separately. The input route and the behavior brief solve different problems.

Handoff judgment: Bolt's GitHub integration can import existing projects and keep project history outside Bolt. That gives engineering an inspectable code handoff and a way to continue work elsewhere. The useful handoff includes the brief and known limitations alongside that code, especially if the prototype deliberately simulates success. Bolt GitHub documentation.

Bolt's pricing FAQ explains that project-file context is a major source of token use. Keep unrelated project scope out of the experiment where possible. The size of a token allowance is not a reliable estimate of how many complete onboarding prototypes it buys.

AI prototyping tools for product managers: who should pick what

Choose by the source of truth, then the behavior you need to observe. That rule is more defensible than an unmeasured claim that one product is universally fastest.

If you have only a product requirement, start by evaluating Magic Patterns as a dedicated prototype workspace. If the approved work already lives in Figma, put Figma Make first and inspect how its kit represents your components and states. If engineering expects to continue a repository, make v0 the first candidate and review its starter before generating the whole flow.

Choose Lovable when a small app-style simulation is the desired artifact, while checking the distinction between its native design-system projects and Enterprise package wrappers. Choose Google Stitch when linked screens can answer the research question, and require an additional behavior check before using the result for data-entry testing.

Choose Claude Design when the useful context is already in a Claude conversation and its supported output suits the next owner. Choose Bolt when an existing code project is central to the experiment, paying attention to the documented Figma import mode and system-plan boundary.

The decision flips when the handoff requirement changes. A founder collecting feedback on wording can accept a different artifact from an engineer expected to reuse the production input component. Agree on that destination before purchasing a plan or polishing the screens.

Give the next owner the accepted flow, the source assets or component package, and a short record of what is simulated. “Finish confirms locally; it creates no account” is a useful note. “Everything works” is not precise enough to guide implementation.

Free prototyping tools: choose an experiment, not an allowance

Free access is enough to begin evaluating a workflow, but it does not guarantee enough capacity to finish a particular prototype. Magic Patterns, Figma, v0, Lovable, Claude and Bolt all publish free entry options; Google says Stitch is currently free of charge. Their makers' pricing and cost pages are linked in the comparison table.

The limits are expressed in different units. A credit can represent different amounts of work within a product, and it is not comparable to another vendor's credit. A token is a unit of text processed by a model, not a finished screen. The prototype's context and revision requests affect how much is used.

Start with the written brief and a stopping condition: a shareable flow that passes the behavior checks. Record the plan, the allowance consumed, the remaining defects and whether a reviewer could open the artifact. That record supports a purchase decision; a large advertised allowance alone does not.

For confidential product work, also check the actual preview's access settings before sending it to a participant. A project being private in an editor does not tell you who can open a published link. Use fictional participant details in this example so the prototype remains independent of a production customer system.

The ones to avoid

Avoid Google Stitch as an assumed finished form implementation when your research depends on personalized input and validation. Its documented hotspot and export capabilities are useful, but they do not remove the need to verify those behaviors in the actual artifact.

Avoid Bolt Pro as an assumed package-based design-system plan. The pricing table places dedicated system knowledge and private registry support in Teams. Avoid Lovable Pro as an assumed wrapper for your existing npm library; that route is documented as Enterprise-only.

Avoid Magic Patterns Starter as an assumed entitlement to every custom system import. Its entry export options and its Enterprise custom import service are separate facts. Avoid Figma Starter for a workflow that requires Make kits, which require a paid Full seat.

Avoid making a new workflow depend on standalone Claude Design at claude.ai/design without accounting for Anthropic's closure notice. Evaluate the current in-conversation Design workflow instead. And avoid v0 solely for repository handoff if nobody on the project intends to inspect or continue that repository.

These are mismatches between a requirement and a documented route. They are not claims that any of the products performed poorly in a test that did not occur.

AI prototyping for product managers: run the session

The prototype is ready for a session when a participant can attempt the intended task without you operating it for them. Visual polish matters when it affects comprehension; it is not a substitute for that basic condition.

1. 1

   Give a goal, not a tour

   Ask the participant to set up Northstar for planning a project and confirm the details. Let the interface explain how. Record where the participant hesitates without immediately explaining the intended action.

2. 2

   Include a recovery task

   Ask them to change the goal before finishing, or correct the email they entered. Observe whether they can find the route and understand the current state. Do not infer that every problem is a tool defect; it may be a design decision in the brief.

3. 3

   Separate design findings from prototype failures

   An unclear goal label is a finding about the proposed experience. A button that does nothing is a prototype failure. Record them separately so a broken simulation does not get interpreted as evidence against the product idea.

4. 4

   Reset and hand off the learning

   Use Start over before the next session. Afterward, give engineering the revised brief, the accepted artifact and the behavior checks, including any simulated success or missing implementation.

Your next move is to choose the candidate that fits your existing assets, run the shared brief, and preserve the output and repair log. Only that completed record can establish which tool gets your particular idea to a usable session fastest.

Questions about AI prototypes

What are the 5 most popular AI tools?

This comparison does not establish a popularity ranking. For a prototype, shortlist by the required input, behavior and handoff. Magic Patterns, Figma Make and v0 are the starting candidates here for different workflows, not the winners of a usage survey.

How can AI be used in prototyping?

Provide a written task, existing design or visual reference, then ask for the screens and interactions needed to explore it. Check navigation, errors, data retention and sharing before using the artifact in a session. The Northstar brief above makes those requirements explicit.

Can I create a prototype with AI for free?

Yes, the named tools provide free entry options, and Google describes Stitch as currently free of charge. Limits and plan-gated system features still apply. A free plan is an opportunity to attempt a prototype, not a guarantee that a particular flow can be completed within its allowance.

Which AI tool is best for making projects?

For the bounded prototype in this comparison, evaluate Magic Patterns first for a dedicated prototype workspace, Figma Make for existing Figma context, or v0 for code continuation. Production projects need a broader evaluation than a clickable demonstration.

What's a better AI than ChatGPT?

Better for this task means a workflow that accepts your source material and produces the artifact your reviewers need. This article compares specialized prototyping routes; it does not establish that one general AI model is better than another.

What are some free AI tools for project making?

Magic Patterns, Figma Make through Starter, v0, Lovable, Claude Design and Bolt have free entry options. Google Stitch is currently free of charge. Use the maker links in the table to check the applicable limits before starting a shared project.

Which AI tool is absolutely free?

Google's own Stitch FAQ says it is currently provided free of charge and operates with daily credits. “Currently free” does not mean unlimited use or a promise that pricing will never change. Other tools here have free tiers alongside paid plans.

Which free AI is best for project management?

A project-management system and a prototype of its onboarding are different purchases. This comparison evaluates the latter. It does not rank tools for running live assignments, permissions or reporting across your business.

How can I use AI for my project?

Write the user task and the smallest flow that can test it. Provide existing design context, generate the prototype, verify the acceptance checks and observe a participant. Preserve the findings and the handoff artifact before expanding the implementation.

Use the AI business workflow audit checklist to name the prototype's owner, required behavior and handoff before choosing a paid workflow.