Prompt Engineering
Lilian Weng 研究 入门 Impact: 8/10
This article delves into the basics and techniques of prompt engineering, emphasizing the importance of effective communication with large language models and how to optimize model performance through example selection and ordering.
Key Points
- Prompt engineering is the art of communicating with large language models to optimize outputs.
- Zero-shot and few-shot learning are basic prompting methods, with the former directly inputting tasks and the latter guiding through examples.
- Example selection and ordering have a huge impact on model performance and require fine-tuned design.
- Techniques like graph-based methods and contrastive learning can enhance the diversity and representativeness of example selection.
Analysis
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