Ending AI Slop — Thais Castello Branco, Taste Labs
For subjective agent output, replace vague requests for quality with decomposed brand constraints, then reserve human preference data for style and creativity that resist deterministic checks.
Subjective quality depends on audience, context, and time. Brand adherence becomes more testable when split into **colors, typography, motion, and textures**; Taste Labs says its contributor community includes **over 1,000 experts** across media and styles.
Give coding agents explicit brand components instead of asking for something generally good. Verify alignment and typography directly, while treating style fit and creativity as preference problems that need carefully selected human data.
Subjective quality depends on audience, context, and time. Brand adherence becomes more testable when split into **colors, typography, motion, and textures**; Taste Labs says its contributor community includes **over 1,000 experts** across media and styles. Give coding agents explicit brand components instead of asking for something generally good. Verify alignment and typography directly, while treating style fit and creativity as preference problems that need carefully selected human data. An LLM judge can hallucinate or invite reward hacking, but expert consensus is not universally reliable either. Disagreement about aesthetics may represent valid preferences rather than bad labels, so averaging judgments can erase useful distinctions.
This turns interface taste from a single vague score into a mixed evaluation problem: alignment and typography can be checked directly, while style and creativity require preference data that preserves audience-specific disagreement. It supports giving coding agents explicit design systems, but limits confidence in either LLM judges or averaged expert labels as universal measures of quality.