{
  "id": 1573495,
  "title": "How to Replace Your Agent’s Generic Taste With Your Own",
  "url": "https://urgent.news/2026/08/17/how-to-replace-your-agents-generic-taste-with-your-own",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-17T18:51:48.000Z",
  "source": {
    "name": "HackerNoon",
    "slug": "hackernoon",
    "url": "https://hackernoon.com/how-to-replace-your-agents-generic-taste-with-your-own?source=rss"
  },
  "original_language": "en",
  "account": "When working with a coding agent to design a SaaS product, you may notice that the resulting design contains generic elements such as cream or near-black backgrounds, muted orange buttons, serif headlines, centered hero sections, feature cards, rounded corners, and AI sparkle icons. The agent relies on familiar templates, component libraries, and design conventions due to its exposure to many similar websites. As AI-generated templates proliferate, these patterns are increasingly reproduced across the web. This \"generic taste\" can serve as a reasonable starting point for a general-purpose system, but it may not align perfectly with a product's specific audience and point of view.\n\nTo move an agent away from generic taste, it is essential to treat taste as a preference-learning problem rather than a prompt-writing problem. Describing the ideal screen from nothing rather than relying on vague words like \"clean,\" \"polished,\" and \"modern\" can help the agent understand your preferences better. Comparing two screens and identifying which one belongs in your product can also provide valuable insights. This approach borrows the same learning structure used during preference-based post-training, where people or AI judges compare different responses and choose the better one to train a reward model or optimise the assistant.\n\nGenerating alternatives, ranking them, and explaining the chosen option can help create a local history of your decisions. By storing the comparison, including the task, preferred output, rejected output, reasoning, and reusable principles, you can build a lightweight preference loop around your agent. This loop involves generating alternatives, asking for genuinely different directions, ranking and explaining them, choosing the strongest option, and storing the comparison. Retrieving relevant previous decisions before generating new content can further enhance the agent's performance.\n\nImplementing this workflow does not require elaborate infrastructure. A simple text file, such as taste.md, can hold your principles, while separate folders for accepted and rejected examples can store paired comparisons. These comparisons should include the date, tag, and context of the decision. The quality of your explanations is crucial in teaching the agent, and a well-crafted \"taste.md\" file can provide the agent with a history of how you make choices. Over time, this loop can help the agent develop a more refined taste, moving beyond a list of colours, fonts, and banned patterns to a deeper understanding of your preferences and the reasoning behind them.",
  "summary": "Learn why AI agents default to generic design and how to align them with your taste by generating alternatives, ranking them and preserving every decision made.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}