AI-generated web applications often produce visually convincing but functionally incomplete outputs. The client needed a scalable, high-signal evaluation process to:

• Compare paired apps on design, features, and functionality
• Evaluate prompt interpretation through structured CUJs
• Identify partial implementations, broken rendering, or faulty interaction patterns
• Gather granular ratings and justifications for model tuning

Turing executed two complementary evaluation workflows based on the client’s guidelines:

Paired app voting

Raters received a user prompt and two app implementations,Side A and Side B. They evaluated:

• Rendering quality: Was the app viewable and visually intact?
• UI design preference: Which side produced better layout, hierarchy, and visual polish?
• Feature completeness: Which side implemented the expected functionality more effectively?
• Functionality: Which side was easier to use, more stable, and more responsive?
• Overall experience: Which side better fulfilled the original request?

Each question was paired with a freeform justification and a structured rubric.

CUJ-based evaluation

Raters received a prompt, a Critical User Journey (CUJ), and a generated app. They scored:

• Rendering quality of the initial view
• CUJ clarity such as whether the CUJ a reasonable interpretation of the prompt
• Core feature coverage based on CUJ expectations
• Functional reliability in supporting the journey
• Overall satisfaction as a developer using AI code generation tools

All scores were grounded in actionable, explanation-backed feedback.

QA guardrails

• Incorporated mandatory feedback from prior studies to avoid common rater errors
• Implemented contradiction checks between justifications and ratings
• Conducted QA review rounds and calibration sessions with the client
• Used a centralized tracker and task status monitoring for throughput and consistency

• Delivered two evaluation datasets with aligned scoring logic and structured justifications
• Rated more than 1000 apps for design quality, prompt adherence, and CUJ coverage
• Maintained an average handling time (AHT) of 10–13 minutes per prompt with high rating quality
• Provided granular insights into prompt interpretation, layout consistency, and interaction flaws

Turing’s dual evaluation strategy provided the client with:

• A scoring foundation for preference models focused on UI and functionality
• Structured data for CUJ-aligned code generation analysis
• Clear signals for improving AI frontend generation across multiple modalities