To create a high-quality AI agent of oneself, an expert can't just rely on their public work. They must manually document their nuanced style and judgment into a system of prompts and triggers. This shifts the burden of creating a good AI product from the platform to the creator, asking them to codify their intuition.

AI models fail at great literary writing because they lack an authentic "voice." This voice isn't just a stylistic quirk; it's the product of an individual's unique life experiences and perspective. Since AI lacks this grounding, its writing feels inauthentic, like an imitation of a style without the substance behind it.

Standard benchmarks are insufficient. A more effective evaluation method is a hybrid approach, weighting a human's qualitative 'taste test' (e.g., 70%) more heavily than an LLM judge's automated score (e.g., 30%). This prioritizes subjective qualities like design, usability, and writing style.

Generic AI copy is poor because LLMs learn from the internet's vast, low-quality content. The key to effective AI-generated copy is training it on a user's specific values, personality, and brand voice, moving beyond generic prompts to create something that resonates authentically with a target audience.

The concept of "taste" is demystified as the crucial human act of defining boundaries for what is good or right. An LLM, having seen everything, lacks opinion. Without a human specifying these constraints, AI will only produce generic, undesirable output—or "AI slop." The creator's opinion is the essential ingredient.

To avoid generic AI-generated text, use the LLM as a critic rather than a writer. By providing a detailed style guide that you co-created with the AI, its feedback on your drafts becomes highly specific and aligned with your personal goals, audience, and tone.

When an LLM produces text with the wrong style, re-prompting is often ineffective. A superior technique is to use a tool that allows you to directly edit the model's output. This act of editing creates a perfect, in-context example for the next turn, teaching the LLM your preferred style much more effectively than descriptive instructions.

Despite AI's ability to generate functional code, replicating the nuanced, subjective quality of a specific designer's "taste" remains extremely difficult. Felix Lee, after spending weeks attempting to codify his own taste into an AI model with little success, notes it's a significant unsolved challenge.

According to Dreamer's CEO, the biggest capability missing from LLMs is "taste." By default, AI-generated applications and UIs are generic and identifiable by the model that created them. It requires extensive human effort in prompt engineering and templating to create delightful, non-generic user experiences.