AI Site Builders

Bolt vs Lovable: What Breaks When You Iterate on AI-Built UIs

Q: Do AI website builders create accessibility problems?

They can, especially under iteration. In this experiment both Bolt and Lovable produced a reasonable first build, but every follow-up change introduced new accessibility violations that the prompts never asked for. A color swap stripped focus rings and broke contrast; a button rename created duplicate CTAs. The first generation is rarely the problem. The changes that follow are.

Q: Is Bolt or Lovable better for accessibility?

In this test Lovable held up better. It started at one accessibility violation and finished at four after three changes, versus Bolt's 11 to 29. Lovable shipped tokenized CSS and semantic HTML, while Bolt hardcoded a Tailwind palette and used non-heading card titles. Tokenized, semantic output gives iteration less to break. Neither tool is a substitute for auditing the rendered page.

Q: Why do automated accessibility checkers miss some issues?

Automated tools like axe-core test what they can measure deterministically: contrast ratios, missing alt text, ARIA misuse, accessible names. They cannot judge context. Four buttons labeled Get started each pass, because each has visible text, even though a user cannot tell which plan each one buys. Ambiguous link text, illogical reading order, and duplicate controls need a human or a context-aware check to catch.

Q: How do you test an AI-generated page for accessibility?

Run the audit against the rendered build in the browser, not the prompt or a design file, and re-run it after every change rather than just the first generation. Combine an automated scan for contrast, ARIA, and structure with a manual pass for context-dependent issues like duplicate CTAs and reading order. OverlayQA's accessibility audit runs on any live URL with no design file required.

Q: Does Figma's Check designs catch these issues?

Not these ones. Figma's Check designs lints a design against your design system inside Figma and can flag contrast and drift at the design stage. But the regressions in this experiment never lived in a Figma file. They appeared in the generated code, three prompts deep, after the design phase was over. Catching them requires reading the rendered build.

By Emilia Veras. Last updated: June 9, 2026

Quick answer: AI site builders nail the first build, then break it as you iterate. We gave Bolt and Lovable the same pricing-page prompt, then three ordinary changes (a color swap, a new tier, a button rename). Each change introduced regressions no one asked for: broken contrast and focus order, a scrambled price ladder, and four identical "Get started" buttons. Accessibility violations climbed from 11 to 29 on Bolt and 1 to 4 on Lovable. The build approach decides how much breaks; auditing the rendered build is the only way to catch it.

The first build is the part everyone shows off. It is also the part that matters least. Real products do not ship from one prompt. They get a color change, a new tier, a copy tweak. So I built one pricing page with both Bolt and Lovable, then changed it three times, auditing the rendered build after every step.

What Three Changes Did

Tool	Build approach	First build	After 3 changes
Bolt	Hardcoded Tailwind palette	11 violations	29 violations
Lovable	Tokenized CSS + semantic markup	1 violation	4 violations

The build approach decided how much broke. "Make it green" stripped Bolt's focus rings and pushed contrast failures from 8 to 23. A button rename gave both tools four identical "Get started" buttons. The tooling difference decides how much breaks. It does not decide whether you have to look.

Why the Design Phase Misses This

Figma's "Check designs" lints a design against your design system inside Figma. But none of these regressions lived in a Figma file. They showed up in the code, three prompts deep. The WebAIM Million finds detectable WCAG failures on more than 94% of the top one million home pages, and our audit of 43 Product Hunt launches found a 97.7% failure rate against WCAG AA. AI builders do not invent this problem. They accelerate it.

What to Do With This

Treat the first generation as a draft, and treat every change after it as a new draft too. According to Figma's State of the Designer 2026, 72% of designers use generative AI and 91% say it improves their output. Audit after every change, check the rendered build, and look for the failures linters cannot score.

Frequently Asked Questions

Do AI website builders create accessibility problems?

They can, especially under iteration. Both Bolt and Lovable produced a reasonable first build, but every follow-up change introduced new accessibility violations the prompts never asked for.

Is Bolt or Lovable better for accessibility?

In this test Lovable held up better (1 to 4 violations vs Bolt's 11 to 29), because it shipped tokenized CSS and semantic HTML. Neither replaces auditing the rendered page.

Why do automated accessibility checkers miss some issues?

Tools like axe-core measure contrast, alt text, and accessible names, but cannot judge context. Four buttons labeled "Get started" each pass even though a user cannot tell which plan each buys.

How do you test an AI-generated page for accessibility?

Audit the rendered build in the browser, re-run after every change, and combine an automated scan with a manual pass for context-dependent issues. OverlayQA's accessibility audit runs on any live URL.

Does Figma's "Check designs" catch these issues?

Not these ones. It lints the design inside Figma, but these regressions appeared in the generated code three prompts deep, after the design phase was over.