Ending AI Slop — Thais Castello Branco, Taste Labs

Evaluating AI-generated design becomes more precise when brand requirements are separated from judgments about style. Taste Labs' presentation describes breaking brand adherence into observable requirements such as typography, colors and alignment. A new page can then be checked against those requirements without requiring it to copy an existing page. Creativity and audience fit still call for contextual human judgment. The company argues that disagreements among designers can represent meaningful differences in preference, so training data should preserve who prefers what and in which setting. This provides two different kinds of feedback: whether a design follows specified constraints, and whether its choices suit the intended audience.

“One is that capability follows measurability. So if we can solve the measurability problem or at least part of it, then we can solve a big portion of these domains.”

Thais Castello Branco

“we've made them great at coding but realistically it's actually a fact about code. Code is something that decomposes, it verifies, it executes”

Thais Castello Branco

“It's when you actually actively break from rules and actively break from patterns that you can create things that are subjective and and great.”

Thais Castello Branco

“The average meaning the most likely does not necessarily coincide with like the optimal.”

Thais Castello Branco

“creating high quality data is expensive. It's difficult. It takes a lot of understanding and depth around a specific domain.”

Thais Castello Branco