The Chameleon User Onboarding Benchmark Report 2025

They're not replacing engineers. They're clearing the runway.

The most effective product teams aren’t vibecoding because it’s novel—they’re doing it because it helps them move faster with more clarity and less friction. Not to skip steps, but to tighten loops.

They use AI to collapse idea → feedback cycles and unblock product conversations. Not to ship faster, but to learn faster. Here’s what that actually looks like in practice, based on what we’ve heard and discussed at multiple roundtables with PMs and product leaders at top tech companies:

1. Prototype to think, not to impress

Many teams shared that vibecoded prototypes aren’t meant to wow stakeholders—they’re there to anchor the conversation. You take a fuzzy idea from a PRD or a customer interview, and instead of waiting for design, you spin up a mock version.

Now you have something concrete to share. Something to poke at, critique, and improve. It’s not a replacement for the design phase—it’s a warm-up. And it beats abstract Figma flows that live in a vacuum. Prototypes aren't answers, they’re better questions.

2. Kill bad ideas before they eat your roadmap

One team used a prototyping tool to mock up three onboarding variants, then tested them with users. Two flopped. One showed promise and got prioritized.

Because it felt interactive, the feedback was richer than with mockups. And because it was disposable, no one got attached. It’s easier to throw away work when it only took 2 hours to make.

3. Build internal tools no one has to pretend are “temporary”

Internal tools were a recurring use case. From debugging dashboards to onboarding health trackers, PMs are spinning up rough, usable versions of tools that would never get real engineering time. They’re not polished. They’re not scalable. They’re not meant to be.

But they unblock teams. They prevent Slack chaos. And they often get adopted faster than anything official. If it saves 5 people 30 minutes a week, it doesn’t need a roadmap.

4. Turn live customer calls into live co-creation

One PM shared how they used an AI design tool during a customer call. They typed prompts in real time, iterating the UI based on what the customer said. By the end of the call, the customer was able to see a rough version of a solution.

This wasn’t “we’ll take this back to the team.” It was: “What about this?” And it built trust on the spot. Vibe-coded mockups make discovery feel like delivery.

5. Use AI to accelerate the scoped, not the squishy

Vibecoding isn’t just for prototypes. PMs and engineers are using tools like Cursor or Copilot to speed up execution on work that’s already defined: converting hardcoded flows to config-based ones, reworking copy, or polishing tooltip styling.

But the moment judgment, architecture, or system design is involved, the human brain still wins.

AI is great at helping you build. It’s still bad at helping you decide.

What these teams get right

• They vibe to clarify, not to ship.

• They collaborate earlier, not later.

• They’re comfortable throwing things out.

• They never confuse an interactive UI with a production-ready feature.

“It’s not about skipping design. It’s about showing your thinking before you waste anyone’s time.”

🛠️ Start small, stay scrappy: Across every roundtable, one message was clear—there’s no definitive playbook for this yet. Everyone’s still figuring it out. As one founder put it: “Nobody really knows how to do this. The only way to learn is to get in, get messy, and figure out what works.” The best teams aren’t perfect. They just start early, learn fast, and aren’t afraid to throw things out. Vibecoding is a new skill, and like any skill, you get better by doing.

Where do you sit on the vibecoding maturity curve?

To help you assess your team’s relationship with vibecoding, we mapped out a simple maturity model based on our research and roundtables. It captures how teams evolve—from ad hoc builds that raise eyebrows, to strategic collaboration where AI becomes a true co-pilot. Take a look below. Then ask yourself: Are we still hacking? Or are we starting to systematize?

Before you ship that prototype…

Ask yourself:

✅ Is this helping your team think better—or just bypassing important steps?

✅ If someone inherited this in 6 months, would they understand it—or rewrite it from scratch?

✅ Are you vibing to unlock feedback, or because you’re avoiding a hard decision?

Vibecoding is not a shortcut to done. It’s a faster way to say: “Here’s what I’m thinking, am I close?” And the best teams? They know when to vibe and when to build.

🛠️ Curious what structured vibecoding looks like in practice? In this episode of the podcast How I AI, Ryan Carson walks through his lightweight system using Cursor, turning prompts into PRDs, breaking them into tasks, and shipping clean code without spiraling into chaos.

Stackblitz + emails

Call it vibecoding, agented development, or prompt-to-prototype, whatever makes it sound less cringeworthy in your product review. The terminology doesn’t matter. What matters is that AI has quietly become a collaborator.

You’ve probably seen it: a PM spins up an onboarding flow with no designer, no developer, and no Jira ticket in sight—then posts, “vibed this out real quick.” And somehow, it works.

Own example from slack?!

That’s vibecoding. Not quite no-code. Not quite engineering. Definitely not scoped. But fast, intuitive, and good enough to test an idea before lunch. It’s not a technical method, it’s a cultural moment. A new way of building: AI-assisted, momentum-driven, and mostly unstructured.

Where is vibecoding showing up in SaaS?

Most commonly, in the gray areas between fully-owned systems:

• Internal tools and admin dashboards

• QA workflows and experimentation sandboxes

• Quick fixes to unblock a team without burning a sprint

And increasingly, user onboarding.

What does user onboarding vibecoding actually look like?

• An interactive demo spun up in Replit to test messaging

• A welcome modal co-written with GPT, previewed in Vercel, and shared for feedback

• An onboarding checklist scaffolded with Cursor, sitting one PR away from real users

Sometimes it’s just an artifact for review. Sometimes it makes it into production. And sometimes it becomes the unexpected default.

Feel like we should have an example of one of these included here – sure we could spin up one fast? 

Why onboarding is vibecoding’s favorite playground

Vibecoding your user onboarding feels deceptively easy. It’s visible. It’s high-impact. It often lives as a layer on  your core product instead of inside it. So it’s tempting to throw together a few modals, tooltips, or checklists using whatever React component library you’ve got lying around like React Joyride, Shepherd.js, or Intro.js, and call it a day. Add AI into the mix, and it’s even more seductive:

• You can quickly generate copy

• You can spin up the code

• You can deploy something that seems helpful in hours
  

And the upside is real: small improvements in onboarding often mean big lifts in activation and revenue. But that surface-level simplicity hides a complex truth: user onboarding is not a one-time flow. It’s a system.

It spans personas, use cases, pricing tiers, and product stages. It requires targeting, testing, logic, localization, measurement… and, oh right … resilience. That’s where most vibecoded onboarding breaks down. It was never built to last. Just to ship.

Where vibecoding in user onboarding thrives (and where it falls apart)

🧪 Track 1: AI-assisted prototyping

Tools: Bolt, Lovable, Vercel v0

Used by: PMs, designers, growth marketers, CSMs

This is where early ideas take form fast. These tools let you build interactive mockups that feel real enough to test or present without needing to open Figma or write a line of code. They’re ideal for alignment, validation, and lightweight experiments.

Good for:

• Spinning up three onboarding flows to test messaging and layout before involving design

• Building a simplified onboarding checklist to walk through in a stakeholder sync

• Creating a click-through version of a user settings page to gather early feedback

The catch:
These tools don’t integrate with your actual product, so what you build is functionally isolated.

• Logic isn’t real: clicking a CTA might animate, but it doesn’t update any user state

• Style may look consistent, but because it’s not built with your real design tokens or components, any updates to your system won’t apply so things drift, and maintenance becomes manual.

• Components don’t follow your product’s logic or naming conventions, so nothing is reusable

Bottom line: Perfect for building momentum. Dangerous if mistaken for shippable product.

👨‍💻 Track 2: AI inside your codebase

Tools: Cursor, Devin, GitHub Copilot, Sweep
Used by: Engineers, technical PMs

These tools live in your codebase. They’re not for dreaming up ideas. They’re for speeding up real product work that’s already been scoped. Think of them as extremely fast, very literal junior engineers. If you tell them exactly what you want, they’ll move quickly. If not, they’ll guess.

Good for:

• Updating the empty state content in your onboarding checklist across multiple locales. When your product supports multiple languages, every change, like new copy in an empty state, needs to be made across English, French, Spanish, etc. AI tools can help find and update those strings in the right files, quickly and consistently.

• Refactoring an onboarding tooltip to support multiple user roles. Let’s say you built a tooltip that only shows to admins, but now you need different tooltips for admins and end users. AI can help rework (aka refactor) that logic, splitting it into clearer, role-specific flows, without changing how the rest of the app behaves.

• Writing the boilerplate logic for a new onboarding step. Say you're adding a new step to your onboarding flow—a settings reminder, for example. You still need to wire up how it appears, when it dismisses, and how progress is tracked. This is tedious but predictable. AI can write 80% of this quickly, as long as you give it clear instructions.
  

The catch: These tools know code, but they don’t know your codebase unless you teach them. They won’t magically follow your product’s architecture, naming conventions, or design guidelines. That said, it's not hard to teach them, it just takes clarity. Here’s how you make it work:

✅ Give them a precise prompt: “Update this modal to use SettingsTooltipV2, show it only to new users, and track dismissals via useOnboardingEvents().”
✅ Link to examples: “Follow the pattern in TooltipWelcome.js and TooltipUpgrade.js.”
✅ Call out rules explicitly: “Never hardcode colors—use theme.palette. Always use useAuthContext() for user info.”

With that level of detail, AI tools are incredibly useful. But without it, they’ll guess, and that guess might work, but break three other things in the process.

(insert a short downloadable guide?) 

Bottom line: These tools are great at doing. You still need to be great at directing.

Heads up from the field: One theme we heard repeatedly in our roundtables was that the real slowdown happens when PMs rush the prompt. AI agents aren’t mind readers. As one product leader put it: “The biggest mistake everyone makes is trying to go too fast. If you just slow down and give the AI proper context, everything speeds up.” 

Why vibecoding feels like progress (but isn’t always)

Vibecoding lets you move fast, especially compared to static docs or Figma mockups that go nowhere. Stakeholders can click through something real. Sales can demo it. You’re learning, not waiting. But make no mistake: you’re still looking at a prototype, not a product.

What makes it helpful:

• You can explore ideas quickly

• You get instant feedback

• You bypass long planning loops

What makes it risky:

• There’s no real logic or state

• It ignores your design system

• Engineering can’t build on it without starting over

It’s the kind of thing that looks finished in a pitch deck, but collapses as soon as someone asks, “Can we ship this?” Before we go deeper, here’s a quick framework to help you figure out where your vibecoding efforts live and when you’re crossing into dangerous territory.

TL;DR: Vibecoding gets you moving. But scaling onboarding takes more than momentum. If your flows need to evolve, adapt, and support growth across teams, stop rebuilding what others have already nailed. You don’t need to do it all yourself. You just need to know when to shift gears.

Vibecoding is the easiest it’s ever been to build onboarding. But managing it? That’s still the hard part. Spinning up a new onboarding experience used to be a sprint. Now it’s a prompt.

You don’t need engineers in the room. You don’t need to spec the whole flow. You don’t even need Figma. You just… vibe it out. And that’s the problem. Because while AI made building easier, it didn’t change what happens next:

• Someone needs to localize it.

• Someone needs to test it.

• Someone needs to version it.

• Someone needs to own it.

And if you keep stacking “quick wins” without a strategy, you’re not scaling onboarding—you’re snowballing chaos.

What vibecoding gives you (until it doesn’t)

It’s the perfect sandbox. You get:

• Real user feedback from fake UI

• Cross-functional buy-in without ticket backlogs

• Creative freedom outside design constraints

But that flexibility comes at a cost. Here’s where it usually starts to crack:

• No safeguards No tests, no error states, no audit logs. You learn the hard way—when something breaks and no one knows how to fix it.

• No system integration Vibecoded flows don’t speak your design language. They don’t follow your tokens, themes, or user segmentation logic.

• Compound fragility Each one-off adds friction. One new modal is fine. Five untracked tooltips later, you’ve got an onboarding Rube Goldberg machine.

• Loss of credibility Design sees off-brand artifacts. Engineering sees tech debt. Leadership sees duct tape—and they’re not wrong.

When vibecoding is still the right move

Stick with it if:

• You’re testing a risky idea or throwing spaghetti at the wall

• The flow is internal, disposable, or niche

• You’re unblocking alignment, not aiming for scale

• You’re iterating within a scoped surface, not expanding complexity mid-sprint

Think of it like this: vibecoding is for movement. Not for maintenance.

AI changed the build vs. buy equation, but not the burden

Here’s the illusion: AI made it so easy to build onboarding that buying a platform might feel unnecessary. Here’s the reality: The hard part was never building. It was managing everything after.

The question isn’t “Can we build this?” It’s “Do we want to maintain it?” The moment you’re juggling:

• Multiple segments, roles, and product areas

• Requests from GTM, CS, marketing, support

• Localization, experimentation, governance

• Analytics and iteration at scale

You’re not just shipping a flow. You’re operating a system.

What Chameleon’s seen after a decade of in-app chaos

Some teams do build in-house. Sometimes, that’s the right call—especially if you’ve got a unique stack, tight data governance needs, or a product philosophy that demands total control.

But after 10 years of helping teams build, scale, and rethink onboarding—from seed-stage tools to enterprise-grade platforms—we’ve seen every edge case.

We’ve seen:

• Onboarding hacked into CMSs

• Targeting logic spread across 3 dashboards

• QA teams using Post-its to track modal versions

• In-app messaging rebuilt three times in three years

If you’re building your own DAP-like system, hats off. But just know: the easy part is over. You’re in infrastructure territory now.

When vibecoding isn’t enough

What to do next

Prototype without permission. Use vibecoding to validate ideas and unblock your team.

Know when you’re pretending. A real flow needs more than a working UI. It needs ownership.

Pick the right tools for the right job. You’re not choosing between build and buy. You’re choosing when to stop rebuilding the same infrastructure.

Let’s be real: tools like Cursor, Devin, and Copilot are powerful, but they’re not mind readers. Think of them less as magical engineers and more like enthusiastic interns with instant recall. Super helpful with guidance. But without direction? They’ll build confidently… and incorrectly.

But when it comes to onboarding? That’s where they can shine—because onboarding isn’t one big feature. It’s a hundred tiny ones. And those are exactly the kind of tasks AI can handle.

The real problem with maintaining hard-coded onboarding

Every PM has felt it: the “this should’ve taken 10 minutes” problem.

• You want to change one word in an onboarding modal, but it’s hardcoded.

• You notice unreadable tooltips in dark mode, but design is already onto the next sprint.

• You finally get stakeholder buy-in on a better checklist… and realize engineering needs a full spec just to start.
  

None of these things are hard to do. But they are annoying, repetitive, and easy to de-prioritize. Which is why onboarding UX often degrades slowly, until someone asks why activation dropped 12%.

This is where AI agents actually help

Used right, agents can claw back the hours lost to onboarding friction. They’re ideal for the work no one wants to own:

• Replacing generic copy with segment-specific headlines across a 5-step tour.

• Standardizing spacing between modals that were built six quarters apart.

• Refactoring four one-off onboarding banners into something semi-reusable.

• Cleaning up a UX pattern that “kind of works” but breaks on iPad.

These tools are best for structured, scoped changes inside your product, especially when the alternative is waiting two weeks to fix a spelling error.

The real unlock isn’t AI. It’s local.

AI dev tools don’t care about your staging pipeline. They expect you to be working in your environment. That means you—yes, the PM—need to be able to:

• Run your app locally.

• Preview onboarding changes live.

• Know when something broke before your customer does.
  

Otherwise, every fix goes through a cycle of: 

You’re not vibecoding. You’re just writing TO-DOs.

You don’t need to code. You need control.

You don’t have to be an engineer to take advantage of this. You just need to reduce the lag between idea and output. If you can test a new tooltip or tweak a checklist step yourself, you’ll make better decisions, faster. That’s the difference between “We should fix that next sprint” and “I fixed it before this meeting ended.”

How to actually get value

1. Treat AI like junior devs: Never say “fix onboarding.” Say “Update tooltip copy on step 4 to match this tone and use spacing token X.”

2. Feed them guardrails: Drop in your style guide, naming patterns, and UI examples. If you don’t, they’ll freestyle—and you’ll be the one cleaning it up.

3. Scope tightly: One task. One flow. One prompt. These tools don’t handle ambiguity well.

4. Loop in engineers strategically: Your engineers can use these tools too—but with more precision. Let them own complex logic, while you own the “should’ve been done last quarter” list.

The prototype landed. Sales used it in a call. Your Head of Product called it “slick.” You felt unstoppable… until Engineering took a look.

That’s usually when things fall apart. Because while you were basking in the glow of “we built this in 20 minutes,” your onboarding mockup was quietly accumulating debt it didn’t earn.

It looked polished, sure. But it didn’t use your design system. It ignored naming conventions. It didn’t do anything… it just looked like it might.

And in onboarding, that’s a bigger problem than most realize.

Onboarding isn’t a static page. It’s a logic-driven experience that adapts by user role, lifecycle stage, or plan. It often includes conditional flows, progressive disclosures, and time-based nudges. If a prototype doesn’t account for all of that, it’s not just incomplete. It’s misleading.

That’s why most vibecoded onboarding never survives the jump to production.

What’s missing (and why it matters)

Engineering teams can’t just “clean it up.” They can’t bolt on targeting logic, state persistence, or cross-device compatibility after the fact. The prototype usually gets scrapped and rewritten, often from scratch.

What felt like momentum ends up as another ticket in the backlog, this time with unrealistic stakeholder expectations attached.

How to prototype your onboarding without creating chaos

Onboarding is one of the worst places to fake it. You’re teaching users how to use your product. If the experience is broken, confusing, or inconsistent, it damages trust. So if you’re vibecoding user onboarding, you have to be honest about the intent.

• Flag it clearly: “This is for feedback, not shipping.”

• Don’t promise logic you can’t deliver.

• Use screenshots or real system components if you can.

• Never end a review with “can we just wire this up?”

Prototyping onboarding can be incredibly valuable but only when the team understands it’s a sketch, not a spec. The moment you try to scale it, your throwaway code becomes a real obstacle.