Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

AI is reshaping product design in two ways at once: as a co-pilot that speeds up the design workflow, and as a non-deterministic capability inside products that demands new interaction patterns. This course addresses both dimensions hands-on.

First, participants use LLMs for research synthesis, sitemap/wireframe generation (Relume), text-to-UI and code prototyping (Figma AI, v0, Bolt/Lovable) and UX writing. Second, they master UX patterns for AI-native features—prompt scaffolding, streaming states, citations, trust calibration, feedback loops and human-in-the-loop controls—and apply them to an organisational case.

Who Should Attend?

  • UI/UX and product designers integrating AI tools into their daily design workflow
  • Product managers adding copilot, semantic search or agentic features to their products
  • Frontend developers and design leads working on rapid design-to-code prototyping
  • Prerequisite: foundational UX/UI design and Figma familiarity

Key Takeaways

  • Synthesise interview transcripts, personas and competitive audits with LLMs while grounding outputs in evidence
  • Generate sitemaps, wireframes and interactive UI variants rapidly using Figma AI, Relume and v0
  • Author structured prompts for consistent UX writing, localization and edge-case microcopy
  • Look beyond chat boxes to choose inline, canvas, command-palette or agentic UI patterns
  • Design for latency, confidence/citations, graceful failure and human-in-the-loop approval flows

Curriculum

AI Workflows in Discovery and User Research
  • Extracting themes from anonymised interview notes while verifying source evidence
  • Generating user scenarios, edge-case matrices and sitemaps (Relume)
Generative Wireframing, UI Ideation and Code Prototyping
  • Text-to-UI and interactive component prototyping with Figma AI, v0 and Lovable/Bolt
  • Maintaining visual consistency when generating icons, illustrations and placeholder assets
AI-Assisted UX Writing and Design System Audits
  • System prompts for brand-aligned microcopy, error messages and localisation
  • Using AI assistants for accessibility, contrast and consistency checks
Interaction Patterns for AI-Native Products (AI UX Patterns)
  • Beyond chat: inline actions, autocomplete, shared canvases and multi-step agent progress views
  • Overcoming blank-canvas paralysis with starter prompts and contextual suggestions
Trust Calibration, Explainability and Human-in-the-Loop UX
  • Streaming/thinking states, source citations, confidence indicators and easy undo/edit
  • Workshop: designing an end-to-end AI feature flow and UX review checklist

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