Task-Specificity of Prompting Techniques
No single prompting technique consistently achieves superior performance across all NLP tasks. Effectiveness depends heavily on the specific task, dataset characteristics, and underlying model architecture.
A survey of 39 prompting techniques across 29 NLP tasks found distinct patterns: code-based approaches (Program-of-Thoughts, PAL) excel at mathematical reasoning, metacognitive prompting performs consistently across classification and inference tasks, and verification methods (Chain-of-Verification) show strength in knowledge-intensive tasks requiring factual accuracy.
The implication: practitioners should match technique to task rather than seeking universal solutions.
Related: 05-molecule—prompting-techniques-by-task-type, 05-molecule—chain-of-thought-prompting, 05-molecule—tool-delegation-pattern