Prompt Engineering for Large Language Models

计算机科学
作者
Andrew Gao
出处
期刊:Social Science Research Network [Social Science Electronic Publishing]
被引量:44
标识
DOI:10.2139/ssrn.4504303
摘要

With the popularization of software like OpenAI's ChatGPT and Google's Bard, large language models (LLMs) have pervaded many aspects of life and work. For instance, ChatGPT can be used to provide customized recipes, suggesting substitutions for missing ingredients. It can be used to draft research proposals, write working code in many programming languages, translate text between languages, assist in policy making, and more (Gao 2023). Users interact with large language models through "prompts'', or natural language instructions. Carefully designed prompts can lead to significantly better outputs.In this review, common strategies for LLM prompt engineering will be explained. Additionally, considerations, recommended resources, and current directions of research on LLM prompt engineering will be discussed. Prompt engineering strategies based on finetuning will not be covered. The goal of this article is to introduce practical and validated prompt engineering techniques to a non-technical audience.
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