E-commerce prototyping with generative AI

The trouble with most ecom prototypes is that they either illustrate a concept but fail to generate decisions. A good prototype offers a quantifiable journey that shows where buyers drop off, what they don’t understand, and what they’re willing to pay for.

Gen AI changes the cost of doing all this. It allows small teams to create multiple testable prototypes of the same idea and iterate from evidence. The tips below concentrate on this goal: prototypes that act like experiments.

“Vibe-code” a microstore that registers real funnel behavior

A prototype that can register a complete click path is better than an imperfect mockup. You want to deliver a “microstore” that has a product page, cart, intention to checkout, and a confirmation step. It can be on a staging domain, accept tests payments and log events. It doesn’t have to be production-level code since its purpose is learning.

Andrej Karpathy described the mindset behind this speed-centric approach: “There’s a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”

A funnel works as an ecommerce prototype because a live microstore exposes problems that a static funnel would obscure.

• Copy that seems innocuous but fails to trigger “Add to cart.”
• Visual hierarchy that falls apart on mobile.
• Trust deficiencies that emerge at final checkout.
• Offer misalignment that causes pogo-sticking on product pages.

And a funnel prototype turns those problems into metrics. It also forces teams to be specific. They have to nail down price, shipping commitment, return copy, and the primary call to action. Those things can be more important than structure.

How to make it—using generative AI in an appropriately pragmatic way

Use generative AI as a builder, producer of parts and templates and then humans as the editor.

Start with narrow request branches; for example:

• One product, one variant
• One checkout page
• One post-purchase confirmation page

Then, extend the prototype via controlled branches.

Branch 1: checkout shape test

Three versions of checkout:

1. One page first
2. Step-per-page
3. Express-pay first

One shape makes no difference to product, price and shipping. That means structure is the only variable. This reduces contention, focuses on friction.

Branch 2: offer framing test

Three versions of product page (only difference is positioning)

For example:

1. Performance first (durability, materials, outdoor)
2. Style first (identity, customization, wearable platform)
3. Risk first (guarantees, returns, shipping)

Equal traffic drive, compare add to cart/begin checkout rates.

Branch 3: trust element toggles

Ask AI for a “trust kit” as modular blocks:

1. Returns clarity panel
2. Warranty line
3. Shipping estimate/cutoffs
4. Payment/credit card security line

Then run a simple toggle test—baseline page vs baseline page +1 trust block.

What to measure—a microstore works when it’s a small set of consistent events

• View product
• Add to cart
• Begin checkout
• Intent to purchase (test purchase)
• Drop-off step

Work with lightweight analytics, server logs or even a simple database table. Consistent naming is key for good comparisons.

The unobvious discipline: treat the prototype as transient code and lasting learnings

Speed is derived from letting the code die quickly. Rigor comes from making the measurement live longer. This is where teams get hung up—they try to build it into production to avoid “bad code” politics that slow learning.

Best rule is simple: Prototype code has a short life.

Prototype learnings become a shared artifact; one pager with metrics, screenshots and next test.

Prototype the modular value layer instead of the core product

Most ecommerce prototypes decorate the core product. Much better is to prototype the modular value layer instead. This is what people engage with emotionally and price mentally before they ever care about product specs. So in practice, this means generative AI can be used to prototype interchangeable elements, configurations or add-on identities as something worth exploring, even if the base product isn’t yet complete.

It applies to anything in any category. Shoes with swappable parts, bags with attachable modules, furniture with reconfigurable facades, supplements with stackable properties. Generative AI allows teams to quickly assess whether customers enjoy the process of choosing, exploring or being delighted by modules and whether that interaction adds value or is disruptive. The prototype effectively asks the question early—do customers want control, curation or surprise?

David Chan, CEO of Davilane puts the logic behind this approach into sharp focus: “The band is functional, but the patch is what people connect with. So when we prototype we start with the part that represents identity, because that’s where people decide to buy.”

This insight generalizes effectively. When there’s a core product that’s more or less set and a meaningful expression layer that’s more flexible, it works better to prototype the expression layer first and learns about demand, pricing and emotional resonance sooner by refining the core. And with generative AI it’s cheap, fast and easy to do just that—exactly what you want for early ecommerce experimentation.

Use “synthetic users” as an adversarial critique panel, then validate with humans

Generative AI spits out “shopper feedback” in seconds. Many teams take it as is and fall into the trap. Because while AI can sound authoritative it may reflect the prompt more than reality. This is why you want to use it in an adversarial way. Generate objections and failure modes, then confirm what matters with humans.

Christopher Roosen, author at ChristopherRoosen.com puts the boundary in plain language: “synthetic users cannot, in any way, pose as real people, for use in Human Centred Design Research.

A critique panel serves a different purpose than research. There is only one goal: pressure-test the experience until it fails so your next human test has sharper hypotheses. For ecommerce, the most expensive mistake is testing something vague like “Do you like this page?” A synthetic critique panel helps you create something more testable.

• Buyers misunderstand compatibility mobile
• Buyers don’t trust shipping even if available but buried
• Buyers need to see why price is justified without comparison

Each one has a clear conclusion that can be built into a prototype in hours.

How to structure the critique panel to get usable output

Generate 6 to 10 roles with strict objectives, strict constraints and strict time limits. Here’s some role playing examples that tend to generate high leverage feedback:

1. Impatient mobile buyer who bails after 1 confusing click
2. Doubter assuming it’s a scam until trusted
3. Buying for a gift needing instant answers on delivery/returns
4. Outdoor buyer wanting durability proof
5. Buyer on a budget needing value framing/anchors
6. Collector caring about drops, modularity and rarity

Give them all the same input: screenshots, copy and microstore url. Then force a structure in output.

Ask for:

• Top 3 objections from first 10 seconds
• Exact element triggering each objection
• One minimal fix per objection that fits within 30 minutes max.

How to validate this without turning it into a massive research project

Once you have your critiques from the panel, choose top 3 that come up most across roles. Then validate using real humans in a lightweight fashion:

• 5 quick usability calls with screen share
• Click test with 2 variants
• Small paid panel test with one question

Use the same format as the AI criticism: “What stopped you?” and “What single change would fix it?” This makes for easy comparison.

The bonus of this one: faster design cycles fewer internal discussions

It’s common for teams to fight over design rationale. Each team has its own criteria for failure. A round of critiques produces a list of “break points” in order of hierarchy. What’s subjective becomes an ordered list:

• Trust fails first
• Clarity fails second
• Persuasion fails third

That might increase conversion for more refinements than appealing aesthetics.

Conclusion

All three tips point to a single system rather than three distinct processes. Fast microstore prototyping produces behavioral evidence. Adversarial synthetic critique renders that evidence into cutting hypotheses. Modular value prototyping directs those hypotheses to the part of the offering that actually creates desire. Even taken independently, each tip is useful. Together, they compress learning cycles in a fashion that conventional ecommerce teams rarely realize.

The creative leverage comes when these processes inform each other. A microstore reveals where users falter. Synthetic critics clarify why they falter and what minimal changes would help. Modular/identity-layer prototypes allow the team to test those changes at a level of resonance rather than appearance. This feedback loop allows generative AI to become a learning multiplier instead of a content creator.