A Cross-Domain Evaluation of Prompting Techniques for Large Language Models
Prompt engineering has become a central mechanism for guiding the behavior of Large Language Models (LLMs) across a wide range of reasoning and language understanding tasks [1], [2]. However, existing studies frequently evaluate prompting techniques using different LLMs, datasets, or task settings, making direct comparisons across methods and domains difficult [3]. Consequently, in this paper we present a controlled cross-domain evaluation of Zero-Shot prompting, Few-Shot prompting, Retrieval-Augmented Generation (RAG), and automatic prompt optimization based on a PE2-inspired optimization pipeline [4]-[6]. All experiments were conducted with the same LLM and evaluated on six benchmark da...
| Authors | Kevin Krazius, Tim Schlippe, Paul Libbrecht |
|---|---|
| Primary category | cs.AI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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