Taste and creative judgment are not innate talents but the result of disciplined effort. True creativity is built by consuming vast amounts of material, relentlessly judging what works, creating consistently, and persisting long enough to improve. It is developed through reps, not a moment of inspiration.

Dylan Field defines taste not as an innate gift but as a point of view developed through a repeatable process. It involves experiencing something, asking "why do I like or dislike this?", and understanding the canon that led to its creation. This allows you to build a framework for judgment.

Technical talent is not the primary driver of resonant creative work. The key ingredient is 'taste'—an unteachable ability to discern what will be emotionally pleasing and impactful to an audience. This intuitive sense separates good creators from great ones.

Concepts like good taste or judgment aren't magical human traits but are a form of "embedded measurement" in our brains. This data, collected through unique, lived experiences (especially edge cases), is not yet digitized and thus remains a key differentiator from AI models trained on public data.

'Taste' is a collection of specific preferences, not an abstract feeling. Document what makes an output 'good' by creating universal rules (e.g., 'write at a ninth-grade level,' 'avoid cheesy quotes,' 'no em dashes'). Feeding these documented rules to an AI transforms your subjective taste into repeatable instructions for consistent results.

True taste is not an innate gift but a developed skill of seeing subtle patterns. By consuming vast amounts of material in a domain—like Kobe Bryant watching game tapes—one builds an intuitive library that leads to refined discernment and unique creation.

To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.

To codify a specific person's "taste" in writing, the team fed the DSPy framework a dataset of tweets with thumbs up/down ratings and explanations. DSPy then optimized a prompt that created an AI "judge" capable of evaluating new content with 76.5% accuracy against that person's preferences.

The best AI models are trained on data that reflects deep, subjective qualities—not just simple criteria. This "taste" is a key differentiator, influencing everything from code generation to creative writing, and is shaped by the values of the frontier lab.