Evaluating text-to-UI models is challenging due to multimodal outputs, subjective evaluation, and subtle quality failures. The client needed a repeatable QA pipeline to:

• Determine whether an AI-generated interface matched the user prompt
• Distinguish between visually polished but semantically incorrect outputs from prompt-aligned designs
• Score results across modes such as mobile and desktop and design variants

Turing implemented a structured visual QA process for prompt-to-UI evaluation.

Prompt decomposition

Each rater interpreted the prompt and outlined "what to expect" from the design. This included:

• Key screens or flows
• Functional elements such as search bars, trip planners, and listings
• Required themes, colors, or content types

This helped build a checklist to validate whether the generated design matched intent.

Multi-screen evaluation

Raters evaluated all generated screens as a holistic product. They assessed:

• Visual appeal including typography, spacing, and colors
• Usability and layout logic
• Design consistency across screens
• Feature and content align with the prompt

Binary pass/fail scoring

Each design was scored as POSITIVE when high-quality and aligned or NEGATIVE when misaligned, broken, or incomplete. Ratings were accompanied by structured reason tags such as:

• Misunderstood prompt
• Visual design flaws
• Missing key features
• Broken components

QA and feedback discipline

• Ratings included written feedback and tagged error reasons
• A centralized tracker ensured sample coverage
• Engineering managers and technical leads resolved ambiguity and reviewed early rater outputs
• Raters followed a structured workflow within a target AHT

• Evaluated more than 250 AI-generated design samples with structured QA feedback
• Enabled benchmarking of prompt-to-UI generation quality
• Maintained consistent visual standards through a five-point rubric
• Created a reusable framework for multimodal interface evaluation

Turing's evaluation protocol helped establish trustable benchmarks for AI-generated UI design. The work enabled the client to:

• Understand layout failures and prompt mismatches
• Compare design modes such as standard and experimental variants
• Fine-tune and train models to generate HTML, Tailwind CSS, JSX, or Figma-compatible layouts