You Don't Need Prompt Engineering Anymore: The Prompting Inversion
This addresses the need for adaptive prompting strategies in AI, showing that optimal methods depend on model capabilities, but it is incremental as it builds on existing prompting techniques.
The paper tackles the problem of prompt engineering for LLMs by introducing Sculpting, a constrained prompting method, and finds that it improves performance on GPT-4o (97% vs. 93% for standard CoT) but harms it on GPT-5 (94.00% vs. 96.36% for CoT) on the GSM8K benchmark.
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.