A Systematic Survey of Prompt Engineering in Large Language Models:
  Techniques and Applications
Pranab Sahoo Ayush Kumar Singh Sriparna Saha Vinija Jain Samrat Mondal Aman Chadha
Prompt engineering has emerged as an indispensable technique for extending
the capabilities of large language models (LLMs) and vision-language models
(VLMs). This approach leverages task-specific instructions, known as prompts,
to enhance model efficacy without modifying the core model parameters. Rather
than updating the model parameters, prompts allow seamless integration of
pre-trained models into downstream tasks by eliciting desired model behaviors
solely based on the given prompt. Prompts can be natural language instructions
that provide context to guide the model or learned vector representations that
activate relevant knowledge. This burgeoning field has enabled success across
various applications, from question-answering to commonsense reasoning.
However, there remains a lack of systematic organization and understanding of
the diverse prompt engineering methods and techniques. This survey paper
addresses the gap by providing a structured overview of recent advancements in
prompt engineering, categorized by application area. For each prompting
approach, we provide a summary detailing the prompting methodology, its
applications, the models involved, and the datasets utilized. We also delve
into the strengths and limitations of each approach and include a taxonomy
diagram and table summarizing datasets, models, and critical points of each
prompting technique. This systematic analysis enables a better understanding of
this rapidly developing field and facilitates future research by illuminating
open challenges and opportunities for prompt engineering.