His drip paintings had made him one of the most famous artists in America. Pollock had accomplished something extraordinarily rare: critical and commercial success achieved in his lifetime. (Later, we’ll examine why it’s so hard for an innovative artist to achieve commercial success in their lifetime.)

Then, Pollock changed direction.

He began making what became known as his Black Pourings. They were darker and more figurative than the colorful, chaotic paintings that had made him famous. Pollock confidently entered a new creative chapter, but his audience was less convinced. The paintings sold poorly and were greeted skeptically by many of the critics and collectors who had embraced his earlier work.

Had Pollock suddenly lost his taste?

That’s not quite the right question.

His artistic vision had changed, but the taste of his audience had not changed with it.

This distinction gets at something fundamental about taste that we often misunderstand. We tend to talk about taste as though it is an objective quality. Someone has “good taste.” Someone else has “bad taste.”

But, I contend… there is no objectively good taste.

Taste is ultimately about making choices among a landscape of possibilities. And whether those choices are good depends on who you are making them for.

Taste is curation

When we think about creative work, we tend to focus on the act of creation: writing the sentence, painting the canvas, composing the song, designing the product.

But I think taste is less about creation than it is about curation.

Taste is the process of looking at a set of possibilities and making a decision about which one is right.

That means one of the most important parts of taste happens before you choose anything. You need to understand the landscape of options.

An interior designer with developed taste doesn’t simply pick the best of the five chairs placed in front of them. She has enough of a sense of the possibility space to know whether the right chair is even there.

The same thing is true of writing, design, film, food, products, and almost any other creative domain.

Taste is not just choosing… it is knowing what could be chosen, deciding what matters, and curating those decisions into an end product that feels right.

But right for whom?

Why you have impeccable taste

Let’s think about taste on a spectrum. At one end of that spectrum is personal taste, or “taste for one”. The creator can create entirely for themselves. Imagine you are choosing a painting or making a photograph to hang in your living room. Your taste is the only taste that matters… there is no reason for your choices to appeal to anyone else.

At the other end is commercial taste: the ability to create something that resonates with millions of people.

Somewhere in between is what we usually call “good taste.”

Good taste is simply the preference of a relatively small set of tastemakers who have been given the authority to anoint particular choices as good or bad. Art curators. Fashion editors. Restaurant critics. Film critics. Design leaders.

Their taste matters if they are the audience you are trying to reach (often because they are the gatekeepers to a much larger audience).

If you are a chef seeking a Michelin star, it matters a great deal whether your taste overlaps with the taste of Michelin inspectors. If you are making a movie primarily for critical acclaim, the preferences of film critics matter. If you’re designing something for a sophisticated design audience, that design community’s conventions and sensibilities matter.

But none of those forms of taste is inherently more legitimate than commercial taste or personal taste.

Sometimes critics and broad audiences agree. Sometimes they don’t. You can see the divergence plainly on sites like Rotten Tomatoes, where critic scores and audience scores can look like evaluations of entirely different movies.

If your goal is to make something millions of people love, the critic’s taste may be irrelevant. The taste that matters is the taste of the audience you are trying to reach.

Taste is not craft

People often conflate taste with craft, but the two are different.

Taste is knowing what you want to create. Craft is the ability to create it.

Everyone has taste. We all have movies we love, music we return to, products we admire, restaurants we recommend. As consumers, we are constantly making judgments about what we like and what we don’t.

But not everyone has the craft to make something that meets the bar of their own taste.

I can watch a movie and know that I love it. That doesn’t mean I can make a great movie. I can appreciate a beautifully designed chair without knowing how to design or build one. I can recognize a great song without being able to compose it.

This is one of the things that separates consumers from creators.

A person with strong taste but little craft can be a great critic: a keen observer of the world around them who can recognize what works, explain why, and distinguish the exceptional from the mediocre.

A creator goes further. They have a sense of what they want to see in the world, and they want to bring it into being. The creator’s ability to close the gap between what they can imagine and what they can actually make is governed by craft.

This is part of what makes creative work so frustrating. Your taste may develop faster than your ability to execute against it. You know the thing you’ve made isn’t right, even if you don’t yet have the skill to make it right.

And this is one of the most exciting things about AI.

AI dramatically lowers the craft barrier.

Someone who has never learned illustration can begin making images. Someone who can’t code can build software. Someone who has an ear for music but never mastered an instrument can begin composing. People who previously had the taste to imagine something but lacked the craft to realize it can begin closing that gap.

When craft democratizes (as it has many times; AI is just the latest step change), an entirely new class of creators is born. That is why Mr. Beast, not Steven Spielberg, is today’s most watched director.

AI lowers the craft barrier, but it also makes the distinction between taste and craft more important… because AI can craft things with little meaningful contribution from the creator.

Today’s opportunity is to use AI to close the gap between what you can imagine and what you can make… while continuing to make the decisions that determine what the thing should actually be.

A creator with strong taste can use AI to make things they never had the craft to make before. A creator who gives up those decisions to AI may be able to create things out of their reach, but those creations won’t reflect the creator’s taste. As we’ll see, this lack of taste is the root cause of slop.

Critique is how you build your taste muscle

Taste develops through an ongoing interrogation of what you experience. We often call that process critique.

Critique sounds like judgment: deciding whether something is good or bad. But useful critique is really a process of asking questions:

• What about this do I like?

• Why do I like it?

And then:

• Do other people like it?

• Why do I think they like it?

You can think about this as a simple two-by-two matrix.

Positive alignment

There are things you like and your audience likes. That’s alignment. Your instincts about what is good overlap with theirs.

Pollock’s drip paintings found that alignment between the artist’s vision and what the audience craves. That’s rare because innovative artists often have a vision the audience has never seen before, and it takes time, sometimes an entire lifetime, for the audience to appreciate that artist’s vision. That’s why Van Gogh died penniless, yet his work is among the most valuable today.

Negative alignment

There are things you don’t like and your audience doesn’t like. There probably isn’t much to learn here. Nuff said.

The interesting quadrants are the other two.

Audience misalignment

There are things the audience likes that you don’t like. This may be the most valuable quadrant to explore when developing taste. The easy response is: People have bad taste. The useful response is: What are they experiencing that I’m not?

Why did this movie become a phenomenon when I found it mediocre? Why does this product resonate? Why does this restaurant have a line around the block? Why does this piece of writing get shared everywhere even though I find the style irritating?

The goal isn’t to convince yourself to like it.

The goal is to close the gap between your model of the audience and the audience itself.

Artist misalignment

Then there are things you like that your audience doesn’t like.

This discrepancy is equally important.

What is it about your sensibility that isn’t landing? Is the concept bad? Is it being expressed poorly? Is it simply wrong for this audience?

Or are you seeing something they haven’t learned to appreciate yet?

This last possibility matters because some of the most interesting creative breakthroughs happen when the maker has a taste the audience has not yet experienced.

Pollock’s drip paintings themselves were not created by asking an audience what kinds of paintings they wanted to buy. His personal sensibility pushed somewhere new, and a meaningful part of the art world was ready to go there.

Pollock’s reception was very different from that of Van Gogh, who experienced rejection for much of his career, which drove him into despair.

That’s the painful tension of creativity.

Lean too hard into what other people like and not enough into what you like, and you lose your personal sense of taste, the thing that makes what you create uniquely yours.

Ignore the audience entirely, and you may create something that only you can appreciate.

Taste lives in the push & pull between the two.

AI can take taste out of the creative process

This is why taste has suddenly become such an important topic.

AI is different from almost every creative tool that came before it in one crucial respect:

AI doesn’t just execute creative decisions. It can make them for you.

A word processor makes writing easier, but it doesn’t decide what your argument should be. Photoshop gives a designer extraordinary capabilities, but the designer still makes decisions about composition, hierarchy, color, typography, and imagery.

Generative AI can take over both.

Ask AI to write an article and it can choose the argument, structure, examples, sentences, transitions, and vocabulary. Ask it to design a landing page and it can choose the layout, hierarchy, copy, colors, images, calls to action, spacing, and visual language.

But those choices are precisely where taste lives.

And if the creator lets AI make too many of those decisions without incorporating their own taste, the result starts to feel like slop.

Well, isn’t slop just temporary? Won’t the models get good enough that slop becomes a thing of the past?

No, never! AI will always produce slop. Let’s look at why.

The models keep getting better, so the defaults keep getting better.

But a better default is still a default.

In writing today, those defaults have recognizable tells: em dashes everywhere, negative parallelisms, overly tidy rhetorical structures, every idea packaged into a neat set of three.

In AI-generated imagery, the tells are different: an overwrought illustrated look, compositions that feel superficially polished but not finely considered, predictable lighting, color, and layout.

Those particular tells will change.

The underlying problem won’t.

Slop is the accumulation of unexamined defaults.

Audience tastes evolve just like an artist’s tastes. What was once “wow”, becomes “meh” when the audience sees too much of it. (If we’re being honest, today’s AI-generated writing is actually pretty good. The problem isn’t quality, it’s familiarity).

The danger of AI isn’t outsourcing the work. It’s outsourcing the decisions. It’s falling prey to generating something the audieence has already tired of.

AI can also strengthen taste

There is another way to use AI.

If taste is curation, AI can give us a dramatically richer set of things to curate.

A writer can explore twenty different openings rather than laboring over two. A designer can quickly see five completely different visual directions. A product builder can prototype several approaches to an interaction before committing to one.

AI can make the possibility space wider.

But only if you stay in the loop as the person making the choices.

The bad workflow is:

Ask → generate → accept.

The better workflow is:

Frame → explore → critique → explore again → select → refine.

Maybe you generate twenty headlines and none of them are right.

That’s information.

Taste is realizing that all twenty are playing in the same conceptual neighborhood and asking for a completely different direction.

Maybe AI gives you a perfectly competent paragraph and you rewrite it anyway. Nothing is technically wrong with it. It just doesn’t sound like you.

That’s taste too.

The lesson isn’t to use AI less.

It is to use AI aggressively for exploration and cautiously for decision-making.

Let AI widen the possibility space. Let it show you directions you wouldn’t have considered. Let it execute choices once you know where you want to go.

But be careful when it silently decides what the thing should be. That’s when the AI’s averages erode your personal sense of taste.

Tell me about a product you love

For years, my favorite product interview question has been incredibly simple:

Tell me about a product you love.

I’ve asked over a thousand people that question. From that starting point, I can talk to someone for five minutes or for an hour.

• What do you love about it? Why?

• What did the makers get right?

• Which decisions do you appreciate?

• Who do you think they were building for?

• What would you change?

More than almost any other question, it helped me understand someone’s potential as a product manager / product builder because it gave me a window into their taste.

But something else was happening during those conversations.

I was developing my own.

Every candidate introduced me to products I might not have encountered, or showed me something in a familiar product that I hadn’t noticed. Their enthusiasm forced me to ask why they saw value where I sometimes didn’t.

That is how taste develops. Not by learning a set of rules about what is good. Not by mimicking the people who have been anointed tastemakers. And certainly not by asking an AI to make the choice for you.

Developing taste is a conversation with the world

Taste is more important than ever. Powerful AI tools are constantly trying to replace our personal sense of what we like or don’t, making it harder to develop the incisive perspective necessary to surprise and delight the people we want to reach.

Taste is not some magical thing that some people have and others don’t. We can all participate in a constant process of refining our taste.

You experience things. You notice your reaction. You interrogate it. You compare it with the reactions of the people you care about reaching. You learn where your sensibility aligns with theirs and where it diverges.

Then you make something.

You put it back into the world…. and the conversation continues.

As teams climb the AI learning curve, they’re accelerating, but new failure modes are also emerging. AI lets anyone on a product team build almost anything. That doesn’t make the team obsolete. It makes the team more important.

Matthew learned this the hard way — in a room with fifteen people.

He was helping a large nonprofit replace its membership and fundraising platform. In two days, with help from AI, he had produced a North Star document and a rough wireframe. Eighteen months earlier, the same work would have taken a designer and at least a week.

…

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(Years later, in another case of magical serendipity, I ended up working for J Allard — creator of that Lemonade Stand game and head of the initiative to launch the first Xbox.)

So other than justifying my gray hair, what’s my point? Though I never ended up writing code professionally, my technical experience has always been helpful as a product builder. It helped me bridge two worlds.

Today, like so many others, I’ve been experiencing a newfound joy in making software. For the first time, AI coding tools enable anyo…

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Do I still have a job?

Here’s my answer, the one I’d give any product builder reading this:

AI didn’t take the PM job. It exposed the one we should have been doing all along.

The hard part of product management was never the paperwork. It was understanding what customers actually want, craf…

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Around the same time, the manosphere’s obsession with male beauty made looksmaxxing the word of the day.

Thus, “tokenmaxxing” was born — and it’s become the AI skeptic’s shorthand for “I told you so.” The party line: productivity gains aren’t showing up in the macro numbers, yet companies are burning millions on AI spend. Business Insider reported that Uber blew through its entire annual AI budget in ju…

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Teams that hadn’t touched AI in early 2024 now use it every day to generate ideas, write PRDs, analyze research, and build working prototypes. I see it in my own work. I went from using AI a few times a day to running three or four threads in parallel, all day long.

Output is up. Velocity is up. Everything looks faster.

And yet, when I sit with product teams — startups, scale-ups, and a few household names — I keep seeing the same picture. Clogged GitHub repos. Stacks of unread AI-generated PRDs. Prototypes in various states of completion. Features abandoned by ambitious individuals who never got the buy-in to get them across the finish line.

We gained something powerful. We also lost something important.

AI has turned the most ambitious people on every team into 10x versions of themselves. But it has also made it harder than ever for them to play together.

Everyone’s doing context engineering in their own silo — generating work that’s overlapping, repetiti…

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AI-powered prototyping has rewritten how we build. The old product development lifecycle has given way to one where working software shows up at every stage of discovery. Teams move faster. Ideas get tested earlier. Engineers stop building features no one wants.

But prototyping is not a cure-all. As teams adopt it, two new failure modes are emerging — both undermining the very promise prototyping was supposed to deliver:

1. The polishing trap: teams spend an inordinate amount of time perfecting prototypes, trying to close the gap between what they’re prototyping and the production software they’ll eventually put in front of customers. They agonize over pixel-perfect spacing. They iterate on details no customer…

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On Artificial Analysis’s intelligence ranking, OpenAI’s frontier models still beat Claude. They’re ahead by the benchmarks that have defined this industry for three years.

On Ramp’s AI Index for May 2026, Anthropic just overtook OpenAI in enterprise spending.

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We’ve all been in some version of this meeting. A 60-minute UX review is running late. The team is poring over different approaches to the same problem. Each approach is plausible. None are provable. The meeting ends in frustration, without a decision.

In a previous post, You might be doing user research wrong, I argued that user research questions come in three flavors:

• Recall questions: What did you do?

• Imagination questions: What would you do?

• Narration questions: What are you doing?

User research is trying to predict the future: what will customers do when we put our shiny new product in front of them? The temptation is to just ask them — what would you do? But imagination questions are the least reliable of the three. Users predict their own behavior about as poorly as we predict it for them.

Good product sense is one solution. It’s a kind of precognition that draws on pattern recognition from years of watching real users. But precognition has a failure mode. When the question is hard — no clear right answer, no obvious precedent, no behavior to recall — product sense devolves into guessing.

I had this type of question recently while working with a startup building an AI-powered news reader. The company has a cold start problem: they can generate great recommendations once they know what users care about — but they need to learn that during onboarding.

They had a taxonomy of over fifty topics across eight categories, and we were caught in a classic debate. Should we collapse topics into categories that users drill into, or show one long scrolling list? Both approaches were defensible. Static wireframes couldn’t tell us which would feel right. We were stuck in our own version of Minority Report — competing visions of the future, no way to verify which would play out.

In the past, we’d have disagreed, tossed a coin, and committed. That’s a guess in disguise. Now, in minutes, we can build prototypes of multiple variants. We can test them ourselves. We can put them in front of customers.

Dazl is one of my favorite new tools for exactly this. It’s purpose-built for product managers — designed to be a teammate across the full product lifecycle, from a first prompt to a handoff-ready prototype. Instead of bouncing between separate tools for PRDs, design, and prototyping, you stay in one place and keep everything in sync.

I gave Dazl a short prompt. Its plan mode generated an editable PRD — feature spec, layout, structure — that I could iterate on before any building started. Within minutes, both versions of the onboarding flow were live: a collapsible topic tree and a long scrolling list. The conversation leveled up from guessing to a discussion grounded in working software — software we could put in front of anyone on the team, or in front of customers.

Suddenly we weren’t guessing; we were validating.

That first prototype was just a starting point. As soon as we wanted something more sophisticated — a real design system, a brand voice, a second variant to compare against — the prototype needed more context than a single prompt could carry. This is where Dazl’s Documents feature earns its keep. You can bring in PRDs, brand guides, design systems, even competitor screenshots as first-class context the AI references while it builds.

In our case, the PRD already lived in Notion. Rather than copy-paste it into a prompt, I connected Dazl to Notion through its MCP integration and pulled the spec in live, so Dazl was working from the actual source of truth, not a stale snapshot. One instruction — update the prototype to match this spec — was enough for Dazl to reconcile what we’d built with what the PRD called for. The second variant, the brand’s tone of voice, and the full design system all landed in a single pass. The prototype stopped looking like a generic AI-generated screen and started looking like the product.

Skills are the other lever that matters as prototypes grow. They’re reusable mini-tools you define once and apply across the whole prototype. The brand guide called for witty, on-brand copy, so instead of hand-editing every headline and button label, we built a witty copy skill once and ran it across all the topics and categories in a single prompt. Once a skill exists, you can pull it whenever you need it instead of re-prompting the same intent over and over.

And then something better happened. While clicking through both variants on mobile, someone noticed neither was using horizontal space well. They suggested a third option: pills. Compact, tappable tags grouped under each category. We added it. Three variants, side by side.

Nobody had wireframed the pill version. It hadn’t appeared in the spec. It emerged from interacting with the first two. The act of building generated an option that no amount of arguing would have surfaced.

This is what painters have always known. They don’t compose in their heads. They sketch — fast, cheap, disposable — to find what works through doing rather than imagining. Sketching is how composition reveals itself.

Product builders haven’t had this. We’ve had specs, which are abstract. We’ve had static designs, which miss interaction. We’ve had MVPs, which are often neither minimal nor viable. So we’ve defaulted to talking (guessing) about what we’d build instead of building to find out.

AI has changed the math. AI prototyping lets us sketch with software. Building is finally cheap enough to use as a thinking tool.

One more thing made the whole loop feel sustainable: Dazl runs in both directions. Once the PRD was loaded as a Document, I could also ask Dazl to update the spec to match the prototype — pulling the new pills variant, the updated copy, and the latest UX details back into the PRD. The spec stopped being a snapshot from week one and started behaving like a living document that tracked what we were actually building.

The newsreader team picked the collapsible version. Sketching told us what we couldn’t have known otherwise: the expanded list felt overwhelming. The pill version was clever on desktop but no better than the long list on mobile. The exercise took less time than the debate would have. And because the variants were real working software, we could publish them and put them in front of users to measure which UI people preferred, which led to more topics selected, and which felt least overwhelming — turning a UX debate into a test we could actually run.

If you’ve prototyped before, you’ve felt this shift. If you haven’t, here’s the move: next time your team is locked in a UX debate, stop arguing and build both. Dazl is a great tool for this. Most AI tools work outside your process, making it difficult to keep everything aligned. Dazl is spec-driven, so as your PRD evolves the prototype stays true to it, and as the prototype evolves the PRD can be brought back in line. Its planning mode will feel familiar to any PM who’s written a PRD, Documents keep your real artifacts in the loop, and Skills let you turn one-off prompts into reusable levers. Engineers will appreciate the underlying code quality too — a clean component hierarchy and stable structure as the prototype grows make the eventual handoff to production smoother.

The goal isn’t a shippable prototype. It’s a better way to decide — turning guesswork into validation.

We used to build to deliver. Now we can build to discover, too.

Thanks to Dazl for sponsoring this post. Try Dazl the next time your team is locked in a UX debate.

And if you want to go deeper, my AI Prototyping course with Reforge starts April 24th.

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My goal was to go deeper than the tutorials out there and help non-technical people become AI-native builders. If prototyping and AI-powered development is reshaping how you approach your role — whether you're a PM, designer, engineer, user researcher, or something else entirely — check it out.

Join AI Prototyping starting April 24th

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We took a three-step checkout flow for a weight loss medication program and replaced it with a twenty-five-step intake. Medical history. Lifestyle questions. Weight loss goals. Personalized projections. The works.

Conversion jumped 40%.

Not despite the added steps. Because of them.

The step-to-step completion rate is north of 99%. Each step fosters their confidence. By the time they reach checkout, they are ready. Confident. The decision feels right.

This flies in the face of conventional wisdom. We’ve been trained to believe that every form field is friction, every additional screen is a drop-off risk. Remove steps. Reduce friction. Get out of the user’s way.

But that advice is incomplete. And following it blindly can cost y…

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Tim invited me to read an early copy, and I was struck by how clearly he’s distilled years of hard-won lessons into a coherent framework. His Progress Wheel is the best unified theory I’ve found for how all the pieces fit together—defining the right strategy, setting meaningful goals, and reducing uncertainty through discovery. It captures what I’ve seen work with high-performing teams: ruthless focus on what matters, not just what looks impressive. If you’re tired of roadmaps that are really just to-do lists, check out Real Progress.

Check out Real Progress by Tim Herbig

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The prototype we’ll build: a music discovery timeline showing how genres evolved from their earliest days. It has good typography, actual album information, real cover art from Spotify, and smooth animations. The kind of prototype you’d put in front of customers, not a wireframe you’d apologize for.

That used to require days or weeks with a full team. Now it takes 30 minutes and a little bit of design thinking.

We’ll cover three things: how to rapidly wireframe your ideas, how to generate realistic data that brings those ideas to life, and how Figma Make combines them into something that feels real.

This post is brought to you by Figma Make.

Figma Make transforms your ideas into interactive prototypes in minutes—no coding required. Start from your existing Figma designs, bring in realistic data, and iterate through conversation. Try Figma Make free and see how fast you can go from blank canvas to customer-ready prototype.

Would you hire an architect who couldn’t draw blueprints?

Imagine you’re about to remodel your house. Your architect has been working diligently and she’s ready to share her plans. You sit down, and she hands you… a Word doc?!

“Trust me, it’s going to be great.”

Would you write the check? Of course not. Architecture is a visual medium. You need to see the structure, the proportions, how spaces connect. Words aren’t enough. You need blueprints.

Digital products work the same way. They’re visual and interactive. You can’t fully communicate a product idea through written specs alone.

For years, I’ve encouraged every PM I’ve worked with to learn wireframing. User experience design is an essential PM competency, not just the designer’s job. As I wrote in How To Become a Peak Product Manager:

The best PMs don’t just describe customer needs, they work with designers to translate those needs into easy-to-use experiences.

Here’s why this matters. When you write “the user taps the album cover and sees track details,” everyone pictures something different. Does a drawer slide up? Does the whole screen transition to a new view? The right answer depends on the user’s intent and the workflow you’re building.

You can’t leave those decisions to chance.

Instead, when you show the interaction visually, everyone sees the same thing. That’s what blueprints do.

Wireframes → a better way to prompt

Most people prompt AI design tools with words. But AI has the same problem your team does: words aren’t specific enough. Software is visual. Visual prompts work better.

Wireframes aren’t just a bridge between product and design anymore. They’re the blueprints that guide your whole team—humans and AI.

From blank canvas to beautiful prototype

Here’s exactly how I went from blank Figma page to working prototype. The process is surprisingly simple once you understand the pieces.

👋 Watch the video to follow along as I go through every step.

1. Start with the Simple Design System

I know, I know. The blank page is intimidating. What font will I use? Does that font size look nice? Should I bold the headlines? How do I create a button? Where do I find nice looking icons?

A few months ago, Figma introduced Simple Design System—available in every new file. Over 400 components, typography presets, colors, icons, and even full layouts.

To get started, I drop an iPhone 16 frame onto the page—again, an easy Figma preset that moves decisions off my plate.

2. Auto-layout everything

My next simplification: use Figma’s auto layout feature for everything. No manual positioning—Figma arranges things vertically or horizontally automatically. I even turn it on for the main iPhone 16 frame since mobile UI is just a vertical stack.

Using auto layout, I add some margins as well. I set 24 pixels for top and bottom margins, 16 pixels on the sides.

Pro tip: use multiples of 4 or 8 for spacing. It keeps everything aligned to a grid even if you don’t know design theory.

3. Rough out your first elements

Let’s design a feature to explore a genre’s history through a timeline of seminal albums.

I want a cover photo at the top. So I add a gray square as a placeholder. Figma Make will fill this in with a real image later.

Name the layer “cover photo.” That naming matters—you’ll see why.

I add a title: “Downtempo.” We’ll build our wireframe around this genre, then use Claude to generate real data—artist names, album titles, descriptions—to make the prototype feel authentic.

The default text looks terrible. Wrong size, wrong font. But this is where Figma’s Simple Design System saves us. It has pre-built typography options. I choose “Title Page” and suddenly the text looks professional. I didn’t make any design decisions—I just picked from styles that already work together.

I add a description below the title, and set the text to the “Body Base” style. It fits in seamlessly.

Next, I add tags for the genre’s most important artists using Figma’s Tag component. Set horizontal auto layout, enable wrapping. Done.

After about 10 minutes, I have a wireframe that looks pretty clean. But it’s still just boxes and placeholder copy.

4. Mix in realistic data

Great wireframes need great copy. The shapes show layout. The words explain the experience.

So far, the copy in my wireframe is “FPO”—for position only (fancy designer term!). I could spend hours researching Downtempo music—writing genre overviews, compiling artist lists, finding album details.

Or I could use AI, which excels at this kind of content generation. Here’s the prompt I gave Claude:

I’m interested in the history of downtempo music. Generate a JSON file that includes the genre name, a description, and an array of 15-20 milestone albums. For each album include: name, release date, artist, a 1-2 sentence description of why it was a milestone, and relevant tags.

Minutes later, I have what I need. Real artist names. Actual album titles. Release years. Well-written descriptions: “Mezzanine by Massive Attack (1998) - This dark, brooding album pushed trip-hop into more experimental territory with its heavy use of guitar samples and collaborative approach with vocalists like Horace Andy.”

This isn’t Lorem Ipsum. This is data that tells a story.

I asked for JSON because structured data is easier for AI tools to parse—important when we build our Figma Make prototype later.

But I’m missing something: album covers. I want real album art, not placeholder images. This is a little tricker for AI.

Claude won’t able to reliably look up album covers without a little help from a friend. I built an MCP server that fetches album covers from Spotify and Deezer. Took about 30 minutes in Claude Code (maybe a future post?). Now Claude can grab the real artwork for any album.

I ask Claude:

Add album covers to this dataset using the album-cover MCP server.

This will take some time. Let’s go back to Figma while Claude does its thing.

5. Finish the wireframe

I want each year to show its important albums. I layout the album UI: cover art, artist name, album name, description, and tags.

Every element is from the Simple Design System and auto layout makes it easy to get the UI laid out nicely. As needed, I pull over some copy from the JSON we generated to get a feeling for how it will look with real data.

In less than 20 minutes, the wireframe is done:

6. Send to Figma Make

Now, we’re ready to build the prototype. Right-click the design and select “Send to Figma Make.”

My prompt is dead simple:

Based on the attached design, build a feature that shows the timeline of a music genre. Use this data to build the prototype:

I paste in the JSON.

Figma Make reads my wireframe—the structure, the layer names, the semantic organization—and generates a working prototype. Real album covers appear in the right places. Artist tags populate at the top. The timeline structures itself by year with albums underneath.

Figma Make understands that the wireframe provides structure and the JSON provides content. It even replaces my placeholder hero image with an actual photo.

No code written. No manual placement of 20 album covers. Just a wireframe, a data file, and AI doing the rest.

7. Iterate with taste

The first version works, but I want to refine it:

Make the cover images larger and add a play button for each album.

Figma Make updates the prototype. Bigger album covers. Play buttons appear on hover with a nice animation.

Let’s change to a dark theme with full rounded buttons. Use this green as the primary color: #1ed760. Use dark gray for the cards #1f1f1f and even darker gray for the background.

The entire design transforms. It starts looking like Spotify. Professional. Intentional.

Add a cool animation to the year as a user scrolls down.

As I scroll down the timeline, each year animates in with a subtle effect. The albums fade in as they enter the viewport.

Each time, Figma Make doesn’t just do what I ask—it adds taste. Hover animations. Subtle fades. Professional polish.

Making the most of Figma Make

Most AI design tools ask you to describe what you want in words. Figma Make flips this: start with a visual, then AI builds from it.

After building this prototype, a few things stood out:

1. First pixel to working prototype in one tool. You wireframe in Figma, generate the prototype in Figma, iterate in Figma. No export/import dance. The entire workflow stays in the canvas where you’re already thinking spatially.

2. Simple Design System removes guesswork. You focus on visual thinking—layout, hierarchy, user flow—not all the details. The components have proper padding, correct spacing grids, typography that scales. You snap together pieces that work.

3. Semantic understanding beats screenshots. Sure, you can paste screenshots into any AI prototyping tool. Figma Make reads your actual layers, components, and structure. Screenshot tools see pixels. Figma Make sees meaning.

4. Built-in taste. Good spacing defaults. Typography that looks right. Animations that feel smooth. You don’t need design expertise to ship something professional.

The result? I went from idea to customer-ready prototype in just 30 minutes.

Better prototypes = better signal

Design-driven prototyping enables teams to accelerate the time from initial hypothesis to initial test. More importantly, it makes that test more reliable by helping teams create better prototypes.

Bad prototypes break the “suspension of disbelief.” Customers have to imagine what the product will eventually look like when it’s “real.” Users act like they’re using the product instead of actually using it. That creates noise.

Great prototypes maintain the suspension of disbelief. They feel real, so users forget they’re testing. That creates signal.

Over to you…

You don’t need years of design training to create something worthwhile.

Next time you have a product idea, don’t write a spec. Open Figma. Drop in a frame. Add simple components. Sketch the structure—what goes where, how it flows.

Then send it to Figma Make and watch it transform into a working prototype. Put it in front of customers. That’s real signal—signal that helps you make better decisions.

With AI, visual thinking is your new superpower.

This post is brought to you by Figma Make.

Figma Make transforms your ideas into interactive prototypes in minutes—no coding required. Start from your existing Figma designs, bring in realistic data, and iterate through conversation. Try Figma Make free and see how fast you can go from blank canvas to customer-ready prototype.

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