Training Taste — Thais Castello Branco, Taste Labs
Taste Labs addresses the problem of 'AI slop'—the homogenization and lack of context in AI-generated content—by proposing a shift in focus from model training to the application layer (inference time). The core methodology involves quantifying subjective domains like design by training 'probes' (small classifiers) on massive datasets (over 2 million websites). This approach allows for the measurement and prediction of slop, which is superior to traditional LLM-as-a-judge methods. Solutions include the 'Brand API' for structuring brand guidelines into machine-readable components and the 'Creativity API' for intentionally generating out-of-distribution content while respecting domain rules.
Key takeaways
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Defining Slop and Greatness
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Slop is defined by three characteristics: repetition, lack of fit (contextual incoherence), and low intent. While defining 'greatness' is subjective, defining slop is easier, as it represents a general sense of soullessness and convergence.
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Measuring Slop Quantitatively
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Taste Labs analyzed over 2 million websites from the past decade to understand design trends. They developed 'probes'—small classifiers—that extract objective features (e.g., contrast, alignment, palette) to predict slop, achieving higher accuracy than LLM-as-a-judge methods.
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Focusing on Inference Time
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The most critical intervention point is the application layer (inference time), where user context and intent are exchanged. Improving quality here is considered equally, if not more, important than improving the base model itself.
