2024 PromptDesignandEngineeringIntro

• (Amatriain, 2024) ⇒ Xavier Amatriain. (2024). “Prompt Design and Engineering: Introduction and Advanced Methods.” In: arXiv preprint arXiv:2401.14423. doi:10.48550/arXiv.2401.14423

Subject Headings: LLM Prompt Engineering, LLM Prompt Design.

Notes

• The paper introduces prompt design and engineering as crucial for maximizing the potential of large language models (LLMs), highlighting the importance of crafting effective prompts for diverse AI applications.
• The paper defines a prompt as the textual input that guides an AI model's output, ranging from simple questions to complex instructions, and underlines the necessity of including either instructions or questions to elicit desired responses from the AI.
• The paper explores basic and advanced prompt examples using LLMs like GPT-4, demonstrating how prompts can vary from direct questions to complex structures like "chain of thought" to improve the model's reasoning capabilities.
• The paper elaborates on advanced prompt engineering techniques such as Chain of Thought (CoT), Tree of Thought (ToT), and Retrieval Augmented Generation (RAG) to overcome the inherent limitations of LLMs like transient state, probabilistic nature, and outdated information.
• The paper discusses the integration of external knowledge through RAG to enrich LLM outputs, making them more informed and contextually relevant by dynamically incorporating real-time or domain-specific information.
• The paper surveys a variety of tools and frameworks developed to aid prompt engineers, such as Langchain, Semantic Kernel, Guidance library, Nemo Guardrails, and LlamaIndex, highlighting their contributions to streamlining prompt engineering processes.
• The paper addresses the inherent challenges in prompt design, including the need to understand the AI model's capabilities, the context of its application, and the creative and domain knowledge required to craft effective prompts.
• The paper underscores the significance of continuous innovation in prompt engineering to keep pace with the rapid evolution of LLMs and generative AI, suggesting that emerging techniques like Automatic Prompt Engineering (APE) may become standard practices.
• The paper concludes by emphasizing the growing criticality of prompt design and engineering in the AI field, encouraging practitioners to engage in this burgeoning area to shape the future trajectory of LLMs and generative AI technologies.** NOTES:
• Prompt Engineering can elaborate on advanced prompt engineering techniques such as Chain of Thought (CoT), Tree of Thought (ToT), and Retrieval Augmented Generation (RAG) to overcome inherent limitations of LLMs like transient state, probabilistic nature, and outdated information.
• Prompt Engineering can discuss the integration of external knowledge through RAG to enrich LLM outputs, making them more informed and contextually relevant by dynamically incorporating real-time or domain-specific information.
• Prompt Engineering can survey a variety of tools and frameworks developed to aid prompt engineers, such as Langchain, Semantic Kernel, Guidance library, Nemo Guardrails, and LlamaIndex, highlighting their contributions to streamlining prompt engineering processes.
• Prompt Engineering can address the inherent challenges in prompt design, including the need to understand the AI model's capabilities, the context of its application, and the creative and domain knowledge required to craft effective prompts.
• Prompt Engineering can underscore the significance of continuous innovation in prompt engineering to keep pace with the rapid evolution of LLMs and generative AI, suggesting that emerging techniques like Automatic Prompt Engineering (APE) may become standard practices.
• Prompt Engineering can conclude by emphasizing the growing criticality of prompt design and engineering in the AI field, encouraging practitioners to engage in this burgeoning area to shape the future trajectory of LLMs and generative AI technologies.
• Prompt Engineering is highlighted as crucial for maximizing the potential of Large Language Models (LLMs), underpinning the development of effective Artificial Intelligence applications through the craft of prompts.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+Prompt+Design+and+Engineering%3A+Introduction+and+Advanced+Methods

Quotes

Abstract

Prompt design and engineering has become an important discipline in just the past few months. In this paper, we provide an introduction to the main concepts and design approaches. We also provide more advanced techniques all the way to those needed to design LLM-based agents. We finish by providing a list of existing tools for prompt engineering.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 PromptDesignandEngineeringIntro	Xavier Amatriain			Prompt Design and Engineering: Introduction and Advanced Methods				10.48550/arXiv.2401.14423		2024