Landing page prompts. Built for v0.
Assemble a v0.dev-ready spec for any landing page: tech stack, mood, sections, CTA, accessibility. Browser-only; nothing is saved.
Brand, audience, mood, sections.
Paste this into v0 or Claude.
What good prompts read like.
Build a single-page landing page for "Hush Skincare". Tech: Next.js + Tailwind. Audience: DTC consumer 28-45, skincare-curious. Value prop: a fragrance-free toner with two ingredients. Mood: editorial-warm, off-white background, moss-green accents. Sections: hero with hero-bottle photo right, ingredient breakdown grid, two before/after testimonials with photo, FAQ on returns and ingredients, final CTA "Shop the toner". WCAG AA. Stack hero on mobile.
Build a single-page landing page for "Tallyloop". Tech: Next.js + Tailwind. Audience: B2B SaaS buyer, mid-market ops manager. Value prop: connect Stripe + QuickBooks; close books 4 days faster. Mood: minimal-tech, slate-blue text, single accent. Sections: hero with product screenshot right, three feature cards, logo-wall social proof, pricing (3 plans, monthly toggle), FAQ on integrations, final CTA "Book a demo". Sticky nav. Visible focus rings.
Build a single-page landing page for "Stillwater Law", a Charleston estate-planning practice. Tech: Astro + Tailwind. Audience: high-net-worth couples 50-70. Value prop: durable trusts written in plain English. Mood: editorial-warm with monochrome accents. Sections: hero with attorney portrait right, three service cards (will, trust, probate), testimonial with photo, FAQ on cost and timeline, final CTA "Book a 30-minute call". Phone-friendly tap targets ≥44px.
A prompt is a brief the LLM can build from.
A landing-page prompt that produces usable code on the first pass does five things at once. It names the tech stack so the model picks the right component pattern. It states the value prop in 200 characters or fewer so there is one anchor for the H1. It lists sections in order with one-line guidance per section. It specifies the CTA verb so the same call repeats consistently across hero and final-CTA. And it constrains the model with explicit no-go items: no carousel libraries, no external animation deps, no analytics scripts. The right granularity is structural; let the model make local design decisions inside each section.
Clarity over cleverness
Prompts that read like a human briefing another human work better than prompts engineered with role-play tricks or reasoning traces. Tools like v0.dev from Vercel and Claude Artifacts from Anthropic are tuned on explicit step-by-step instructions. A short brief that names the audience, the action, and the sections beats a long brief filled with adjective-heavy adjectives. If a junior designer could build the page from your prompt without asking questions, an LLM can too.
Component-aware specs
Match the prompt to the framework. A v0 prompt that targets Next.js App Router should ask for server components by default and explicit "use client" only where interactivity demands it. An Astro prompt should ask for static rendering and component islands. A plain HTML+Tailwind prompt should specify CDN inclusion and a single `<script>` block at the bottom. Without a stack constraint, the LLM defaults to whatever pattern dominates its training data, which is rarely the right answer for your project.
Copy is the spec
The single line of value-prop text in the prompt becomes the H1 of the generated page. Write it deliberately. A vague value prop ("a better way to manage projects") generates a vague headline. A specific value prop ("close your books 4 days faster") generates a specific headline. The 200-character limit forces specificity; if the value prop does not fit in 200 characters, the page will not communicate it in five seconds either.
Constrain the LLM
The most underused part of a build prompt is the negative-constraint block. List what you do not want. No carousel libraries. No external icon library. No analytics scripts. No modal libraries. Cursor and Anthropic's Claude follow negative constraints reliably; v0 follows them most of the time. Without them, you get a Tailwind page bolted to Swiper carousel, react-icons, and three analytics SDKs you did not ask for.
Iterate on the diff
The first build is rarely the final build. Treat prompt-to-code as the first pass of a typical design feedback loop. After the model produces code, review three things: (1) does the H1 match the value prop, (2) do the section headings map to the section list you specified, (3) do the CTAs match the CTA verb. If two of three are off, regenerate; if one is off, edit by hand. Most landing pages reach final state in 2-3 iterations. Tools like Figma's Dev Mode and GitHub Copilot Workspace shorten iteration further.
Performance and accessibility floors
Always include WCAG 2.2 AA contrast as a hard requirement. Core Web Vitals targets - LCP under 2.5 seconds, INP under 200 milliseconds, CLS under 0.1 - should be in the prompt for any page that will see paid traffic. Specify lazy-loading for below-fold images, explicit width/height on every image, and a single web-font at most. Vercel's Speed Insights and schema.org structured data are easy add-ons after the first build.
Related tools: Headline analyzer for the H1 that the prompt produces. Meta tag generator for the page's SERP card. Schema markup generator for structured data. Email design prompt generator for the post-conversion email. Web design service for the human-led version.
Five answers.
What is a landing page prompt generator?
A tool that assembles a structured natural-language spec describing a landing page so that an LLM-driven builder like v0.dev, Claude Artifacts, Cursor, or Windsurf can generate component code in one pass. The prompt covers tech stack, brand mood, color guidance, section list, CTA copy direction, accessibility notes, and responsive notes.
Which AI builders does this prompt work with?
v0.dev (Vercel), Claude Artifacts, Cursor, Windsurf, Lovable, Bolt.new, and Replit Agent. Any tool that accepts a natural-language brief and returns React or HTML+Tailwind code can use this prompt without modification.
Why does the prompt include a tech stack constraint?
Stack-specific output reduces rewrite cost. A v0 prompt that specifies Next.js App Router gets server components; an Astro prompt skips client-only patterns. Without a stack constraint, the LLM defaults to whatever pattern dominates its training data, which is rarely the right answer for your project.
How specific should the section list be?
Specific enough to anchor structure, vague enough to let the LLM make local design decisions. Hero plus three feature cards plus social-proof plus pricing plus FAQ plus final-CTA is the right granularity. Listing every sub-component (logo, nav-link, button-state) over-constrains the model and produces brittle output.
Does this tool save my prompts?
No. Every value you enter and every prompt assembled lives in memory for this browser tab only. Nothing is transmitted to a server, stored in a database, or synced across devices. Close the tab and the data is gone. The Copy prompt button puts the assembled text on your clipboard.
Three mistakes we see most.
The single largest reason AI-generated landing pages convert at 0.4% instead of 4% is the prompt, not the model. Nielsen Norman Group's landing-page research repeatedly finds that audience-clarity in the opening line predicts conversion more reliably than design polish; a generic prompt produces a generic page that fails the 8-second comprehension test.
Mistake 1, leading with brand instead of audience problem: teams type "build a landing page for Acme, a SaaS startup". The model has no anchor and defaults to a hero like "Acme: The next-generation platform". That headline names the company and says nothing about who it serves or why. Nielsen Norman's value-proposition research shows users decide whether to read on within 10 seconds based on whether they recognise themselves. Start the prompt with audience and problem: "Series A founders losing 20 hours a week on payroll reconciliation". Brand goes in the logo, not the H1.
Mistake 2, ungrounded benefit claims: "save time, increase revenue, scale faster" reads like every SaaS page since 2014. The model writes whatever you allow it to. Claude's prompting documentation on grounding is explicit: specific numbers, named integrations, and concrete artefacts produce specific output. Feed the prompt your real metrics, your real customer quotes, your real stack names. "Reduces month-end close from 5 days to 4 hours, plugs into NetSuite, used by Mercury Bank" outranks "save time" every time.
Mistake 3, wireframe-first thinking: prompts that specify "hero, then features, then pricing, then testimonials, then FAQ" reverse the work. The order of a landing page is dictated by which question a visitor asks at which scroll depth, not by a template. Write the message first (audience, promise, proof, ask), let the model propose the structure. The page that orders sections to match user questions outconverts the templated five-block layout by 30 to 90% in our A/B test history.
When to actually use this: at the start of any new page build (kill the blank-canvas tax), when refreshing existing pages that under-perform on heatmaps, and when feeding briefs to writers or AI builders like v0, Lovable, or Claude Artifacts. Pair the generated prompt with a real landing page design engagement when conversion lift matters.
Related Digital Heroes services + reading: See our Landing page design service for production-grade conversion work, plus our Web design service for full-site builds. Sibling tools: Website Hero Prompt Generator and Ad Creative Prompt Generator.
Prompts get you draft one.
Our web-design engagements take the v0 draft and ship it: copywriting, photography, brand-system tokens, structured data, accessibility audit, paid-traffic-ready performance budget. 2-week sprint.
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