Persona-based approaches contextualise AI responses within specific roles, expertise domains, or cognitive frameworks, thereby improving both relevance and quality of generated outputs (Kong et al. 2024). These techniques encompass mega prompts providing extensive contextual information, expert prompts assigning specific professional roles, and advanced reasoning frameworks such as Tree of Thoughts
Example-driven prompts leverage demonstrations to guide large language models (LLMs) in producing precise and contextually relevant outputs, forming a critical component of prompt engineering. This category includes zero-shot prompting, one-shot prompting, and few-shot prompting, each offering varying degrees of guidance for research tasks. These techniques enable researchers to tailor LLM
Prompts are central to human-AI interaction, with their complexity directly influencing the performance of large language models (LLMs). Within prompt engineering, prompts can be categorised by their structural and functional complexity. This section focuses on three core prompt types: short, general questions or instructions; longer, specific questions with defined output
