LLM prompt engineering: Specificity beats politeness, research shows

Prompt engineering best practices are evolving, with new research suggesting that elements like politeness and persona do not reliably improve LLM performance. Studies from The Wharton School and findings from EMNLP 2024 indicate that while specific instructions on output format and constraints can significantly impact results, the inclusion of "magic words" or detailed persona descriptions often adds noise rather than value. Companies like OpenAI and Anthropic are also refining their guidance, emphasizing conciseness and directness in prompts to achieve better, more predictable outcomes. AI

IMPACT Refines understanding of effective LLM prompting, potentially leading to more efficient and predictable AI interactions.

RANK_REASON The cluster discusses findings from academic research and industry studies on LLM prompt engineering. [lever_c_demoted from research: ic=1 ai=1.0]

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• Anthropic
• Ethan Mollick
• GPQA Diamond
• MMLU-Pro
• OpenAI
• The 2024 Conference on Empirical Methods in Natural Language Processing
• The Wharton School

• paper
• product

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