Prompt Engineering for Large Language Models
Open accessAbstract
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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Prompt engineering has emerged as a key strategy for engaging LLMs and has been shown to improve LLM responses.
“Prompt engineering has emerged as a key strategy for engaging LLMs, involving structuring queries with clear context, instructions, and expected outputs, 119 and has been shown to improve LLM responses.120”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [119]
The prompts used to query the LLM significantly affect response quality and LLM usage costs.
“The prompts used to query the LLM also significantly affect response quality and LLM usage costs.53,103,119”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [119]
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Cited by 68
- Authors
- Gao, A.
- Affiliations
- Stanford University (US)
- Corresponding author
- Gao, Andrew
- ORCID
- Gao, Andrew
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4384519236
- Cited by
- 68
- References
- 0
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 17 · 2023: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- prompt engineering, large language models, natural language prompts, ChatGPT, Google Bard, prompt strategies, LLM applications, conversational AI
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