An Empirical Categorization of Prompting Techniques for Large Language
  Models: A Practitioner's Guide
Oluwole Fagbohun Rachel M. Harrison Anton Dereventsov
Due to rapid advancements in the development of Large Language Models (LLMs),
programming these models with prompts has recently gained significant
attention. However, the sheer number of available prompt engineering techniques
creates an overwhelming landscape for practitioners looking to utilize these
tools. For the most efficient and effective use of LLMs, it is important to
compile a comprehensive list of prompting techniques and establish a
standardized, interdisciplinary categorization framework. In this survey, we
examine some of the most well-known prompting techniques from both academic and
practical viewpoints and classify them into seven distinct categories. We
present an overview of each category, aiming to clarify their unique
contributions and showcase their practical applications in real-world examples
in order to equip fellow practitioners with a structured framework for
understanding and categorizing prompting techniques tailored to their specific
domains. We believe that this approach will help simplify the complex landscape
of prompt engineering and enable more effective utilization of LLMs in various
applications. By providing practitioners with a systematic approach to prompt
categorization, we aim to assist in navigating the intricacies of effective
prompt design for conversational pre-trained LLMs and inspire new possibilities
in their respective fields.