{
  "id": 10351540,
  "title": "Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity",
  "url": "https://urgent.news/2026/09/25/prompt-minimization-reducing-input-redundancy-without-sacrificing",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-25T16:45:28.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.31505v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Despite the growing capabilities of large language models (LLMs), prompt design remains largely heuristic and ad hoc. This project will explore $\\textit{prompt minimization}$, the process of reducing prompts to their smallest, most information-dense form while preserving output fidelity. Practically, shorter prompts reduce computational overhead and inference latency, especially when large…",
  "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."
}