Prompting methods show no clear winner in guiding large language models
A Cross-Domain Evaluation of Prompting Techniques for Large Language Models
Artificial IntelligenceComputation and Language
Summary
Prompting techniques are ways to tell large language models what to do. The authors tested different prompting methods on the same model and tasks to see which works best. They found that no one method is the best across all kinds of problems, and performance depends a lot on the task type. Some methods work better when extra information is available, while others suit simpler tasks. This means the best prompting approach should be chosen based on the specific task rather than expecting one method to work everywhere.
What this means in practice
- •For natural language processing teams: Select adaptive prompting strategies tailored to task characteristics for improved model outputs in language tasks.
- •For knowledge management teams: Implement retrieval-augmented prompting when relevant examples are available to enhance information accuracy in language models.
Authors
Kevin Krazius, Tim Schlippe, Paul Libbrecht
Abstract
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 datasets covering arithmetic reasoning, logical reasoning, commonsense reasoning, and factual verification. The results show that no prompting strategy consistently outperforms the others across all domains, and pairwise statistical comparisons of the highest-performing configurations revealed no significant differences. RAG achieves the highest overall average accuracy of 78.33% when 300 examples are available for retrieval, while Few-Shot Prompting achieves comparatively high accuracies across several structured reasoning tasks. Zero-Shot Prompting remains competitive on tasks with self-contained inputs, whereas the PE2-inspired optimization approach produces stable but not systematically superior results compared to the other evaluated prompting techniques. Furthermore, increasing the number of task-specific examples does not consistently improve performance across prompting techniques, indicating strong domain and task dependency. These findings suggest that prompting techniques should be selected adaptively based on task characteristics rather than treated as universally applicable optimization strategies.