Case Study: Vibecoding a Weather app

Role: UX Designer
Tools: Lovable, ChatGPT, GA4, Met Office API
Focus: AI-assisted design, rapid iteration, accessibility, and analytics-driven UX

Vibecoding a weather app using the Lean UX framework and APIs

AI-assisted app builders enable rapid development, but without a UX structure, they can lead to inefficient iteration, unclear requirements, and wasted resources.

The challenge was to design and build a functional, accessible weather app using Lovable while maintaining UX rigour, minimising credit usage, and ensuring the product was measurable, usable, and scalable.

I treated Lovable as a co-designer rather than a code generator, applying a standard UX framework: planning, validation, iteration, and analysis, throughout the build.

[Sample Generator Block]

  • Started in Planning Mode to reduce credit usage and prevent premature execution.
  • Used Lovable’s scoping questions (audience, location, purpose, visual direction) as a lightweight discovery phase.
  • Reviewed and refined the autogenerated plan before approving it, treating it as a design specification rather than a final solution.
  • Evaluated data-layer options and selected the appropriate database approach.
  • Validated external API requirements by confirming the Met Office API did not require a client ID, unblocking integration early.
  • Updated the background imagery post-render to better align with the product tone.
  • Added a hero icon to the title screen to strengthen visual hierarchy and branding.
  • Introduced a persistent header and footer to improve navigation and structure.
  • Added a 7-day weather forecast to increase the depth of information.
  • Implemented a location search feature to support multiple cities and user control.
  • Inspected the backend structure to better understand how Lovable assembled the app, enabling more intentional edits.
  • Applied ARIA roles and descriptive labels to ensure screen-reader compatibility.
  • Added structured data markup to improve search engine understanding.
  • Validated schema using Google’s Rich Results Test.
  • Delivered a fully functional, accessible weather app with:
    • Dynamic location search
    • Extended forecasting
    • Analytics instrumentation
    • SEO and accessibility best practices
  • Achieved rapid iteration without unnecessary credit usage by separating planning, rendering, and post-render edits.
  • Maintained UX control in an AI-driven environment by validating assumptions before execution.
Added an llms.txt file to guide LLM indexing and interaction.
Explicitly excluded sensitive infrastructure details and security-relevant information to reduce risk exposure.