Prompt engineering stands as a central pillar in the realm of LLMs, tailoring inputs to extract superior outcomes. This comprehensive review offers insightful examination of several methods such as chain-of-thought and tree-of-thoughts prompting.

Survey revelations:

• Role-Prompting: Defines various roles for inputs, guiding LLMs towards more accurate responses.
• Few-Shot Prompting: Employs minimal examples to adapt LLMs to new tasks effectively.
• External Knowledge: Addresses ‘machine hallucination’ by integrating external data through plugins.
• Application Scope: Highlights the impact of prompt engineering in sectors like education and programming.

Implications for future research: This survey emphasizes the vast possibilities and obstacles in prompt engineering, urging a deeper exploration into its potential. It underscores the importance of understanding the dynamics between AI-generated content and the prompts that guide it, and calls for varied methods to assess these interplays. Read more