The 70% Rule: When Axiomatic Prompting Helps, and When It Hurts
Wasserman, Adam Zachary · Zenodo (CERN European Organization for Nuclear Research) · 2026
We introduce axiomatic prompting, a technique that augments LLM classification prompts with explicit decision rules of the form pattern →category. Across six classification tasks and eight models spanning five providers (OpenAI, Anthropic, Google, Meta, Alibaba), we find a consistent threshold effect: axiomatic prompting significantly improves accuracy when zero-shot performance falls below 70% (gains of +15–24%, p < 0.05), but significantly hurts performance above this threshold (losses of -5 to -20%, p < 0.05). We term this the 70% Rule, a simple heuristic that predicted the direction of axiomatic prompting’s effect with 100% accuracy in our experiments. Ablation studies reveal that axiom style matters: pattern-based rules derived from data outperform conceptual definitions. Our findings suggest practitioners should evaluate zero-shot baselines before investing in rule-based prompt engineering. Code and data available at https://doi.org/10.17605/OSF.IO/PCX2D.