You Don't Need Prompt Engineering Anymore: The Prompting Inversion

📝 Original Info

• Title: You Don’t Need Prompt Engineering Anymore: The Prompting Inversion
• ArXiv ID: 2510.22251
• Date: 2025-10-25
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (필요 시 원문에서 확인 바랍니다.) **

📝 Abstract

Prompt engineering, particularly Chain-of-Thought (CoT) prompting, significantly enhances LLM reasoning capabilities. We introduce "Sculpting," a constrained, rule-based prompting method designed to improve upon standard CoT by reducing errors from semantic ambiguity and flawed common sense. We evaluate three prompting strategies (Zero Shot, standard CoT, and Sculpting) across three OpenAI model generations (gpt-4o-mini, gpt-4o, gpt-5) using the GSM8K mathematical reasoning benchmark (1,317 problems). Our findings reveal a "Prompting Inversion": Sculpting provides advantages on gpt-4o (97% vs. 93% for standard CoT), but becomes detrimental on gpt-5 (94.00% vs. 96.36% for CoT on full benchmark). We trace this to a "Guardrail-to-Handcuff" transition where constraints preventing common-sense errors in mid-tier models induce hyper-literalism in advanced models. Our detailed error analysis demonstrates that optimal prompting strategies must co-evolve with model capabilities, suggesting simpler prompts for more capable models.

💡 Deep Analysis

📄 Full Content

Reference