How to Write Vibe Prompts That Generate Consistent, Production-Ready UI for Your MVP

A reliable vibe prompt has two layers. The first layer is the product brief: who the user is, what they need to accomplish, and what the screen should help them decide. The second layer is the UI contract: rules that should apply to every generated screen. Separating these layers prevents the AI from confusing a one-time screen request with a global design decision. Start with a concise product context. For example: “Create a customer portal for a local healthcare provider. Patients need to review upcoming appointments, complete forms, and request a time change.” This is more useful than “Build a modern healthcare dashboard,” because it identifies the audience, domain, and core tasks. Avoid adding speculative features simply because they sound impressive. Every additional route or interaction creates more opportunities for inconsistency. Next, define the design language using roles rather than isolated decoration. Say that the primary color is used for high-intent actions and active navigation, while the accent color is reserved for status or emphasis. Specify a type scale, spacing rhythm, surface hierarchy, and component shape. A token is a named design value, such as “color.text.primary” or “space.4,” which lets multiple screens refer to the same decision instead of inventing new values. Finally, describe behavior. Tell the generator how the interface responds to narrow screens, slow network conditions, missing records, validation failures, and permission limits. These details are not optional polish. They determine whether a user can complete a task when the data is incomplete or the expected path does not work. For accessibility decisions, use the W3C Web Content Accessibility Guidelines 2.2 as the reference point rather than relying on subjective language such as “make it accessible.”

You can adapt the following template for a SaaS dashboard, customer portal, marketplace, or internal tool. Keep the global section stable across screens, then add a short screen-specific section at the end. This reduces prompt drift, where each new request gradually changes the product’s visual language. “Build the [screen or flow] for [primary user] who needs to [task]. Product personality: [three to five adjectives]. Keep the experience [specific behavioral qualities, such as calm, direct, and easy to scan]. Use this design system: font [family or category]; type sizes [body, label, heading, display]; colors [background, surface, text, muted text, primary action, success, warning, danger]; spacing based on [4 or 8] pixel increments; radius [small, medium, large]; shadows [none, subtle, elevated]; icons [style and stroke weight]. “Use these shared components: [navigation], [buttons], [inputs], [cards], [tables], [badges], [alerts], and [dialogs]. Reuse the same component for the same purpose across all routes. Use sentence case labels, visible field names, clear action verbs, and one primary action per section. Do not add gradients, decorative illustrations, or new colors unless they support a stated user task. “Support these states for every relevant component: loading, populated, empty, error, disabled, and permission-restricted. Test with long names, long addresses, missing images, zero records, duplicate records, failed requests, and a slow connection. At 320px, 768px, and 1440px, preserve readable hierarchy, avoid horizontal scrolling except where a table genuinely requires it, and keep actions reachable. Provide visible keyboard focus, logical tab order, labels for inputs, text alternatives for meaningful images, and error messages that explain how to recover. Return the interface with a route list, component list, data assumptions, and an acceptance checklist.” For a screen-specific addition, write: “Screen requirements: show current subscription status, invoices, payment method, and a button to update billing. If there are no invoices, explain why and show the next expected billing event. If payment data cannot load, preserve the page structure and offer a retry action.” This is more durable than asking for a “beautiful billing page.”

The most common mistake is treating visual adjectives as a complete specification. “Minimal, modern, and premium” can describe thousands of interfaces, so the output will depend on the generator’s assumptions. Correct this by pairing each adjective with visible evidence: a limited color palette, a defined spacing rhythm, restrained motion, explicit hierarchy, or a particular content density. Another mistake is requesting every screen in one enormous prompt. Large prompts often mix global rules, route-specific requirements, data assumptions, and future ideas. The result may look comprehensive but becomes difficult to review. Establish the shared system first, generate one representative flow, then add routes while repeating the acceptance criteria and preserving the same token vocabulary. Do not use screenshots as the only source of truth. A screenshot shows a single content state and viewport, not what happens after a failed payment, an expired session, or an empty search. If you use a visual reference, describe what should be copied and what should not be copied. Never assume that a visual imitation includes the underlying interaction, data model, or accessibility behavior. A fourth mistake is allowing the AI to invent business rules. For example, a checkout prompt may produce a discount field without specifying eligibility, expiration, currency, or failure handling. Separate known requirements from open questions. Ask the generator to flag unresolved assumptions rather than silently turning them into product behavior. For integrations, map the data and error paths before polishing the UI, using a framework such as mapping MVP integrations and data flows. Finally, do not approve a screen because it looks correct with static data. Run the generated interface through a small test matrix: one normal record, no records, many records, malformed or missing optional data, a slow response, a rejected action, and a user with limited permissions. The practical risks behind this process are explained in why attractive prototypes fail with real data.