prompt engineering

Introduction

Prompt engineering is a critical technique for optimizing the performance of large language models (LLMs) by crafting effective prompts that guide the models to produce desired outputs. This process involves designing, refining, and implementing prompts to enhance the interaction with LLMs across various applications, including natural language processing, vision-language models, and medical education.

Key Insights

Importance and Challenges of Prompt Engineering:
- Effective prompt engineering is essential for achieving high-quality outputs from LLMs, and poorly constructed prompts lead to unsatisfactory results .
- The process requires complex reasoning and systematic approaches to communicate tasks clearly and optimize model performance .
Techniques and Strategies:
- Various prompt engineering techniques, such as step-by-step reasoning templates and context specification, have been shown to improve LLM performance .
- Catalogs of prompt patterns provide reusable solutions to common problems, enhancing the effectiveness of LLM interactions.
- Model-driven engineering and domain-specific languages (DSLs) can support the prompt engineering process by defining platform-independent prompts adaptable to different AI systems.
Applications in Different Domains:
- In medical education, prompt engineering can personalize learning, provide interactive engagement, and ensure accurate and unbiased responses from GLMs .
- Vision-language models benefit from prompt engineering by adapting pre-trained models to new tasks without updating model parameters, facilitating applications like image-text matching and text-to-image generation.
- Creative fields, such as AI art, leverage prompt engineering to enable users to create digital content effectively, though it requires acquiring specific skills and vocabulary.
Automatic Prompt Engineering:
- Automatic Prompt Engineer (APE) methods can generate and select effective prompts automatically, outperforming human-crafted prompts in various NLP tasks.
- These methods optimize instructions by searching over a pool of candidates proposed by an LLM, improving zero-shot and few-shot learning performance.

Conclusion

Prompt engineering is a vital skill for optimizing the performance of large language models across various applications. Effective prompt engineering involves systematic approaches, reusable patterns, and domain-specific techniques to enhance model interactions. In medical education, vision-language models, and creative fields, prompt engineering enables personalized, accurate, and high-quality outputs. Automatic prompt engineering methods further advance the field by generating superior prompts, demonstrating the potential for continuous improvement and innovation in this area.

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Prompt engineering

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Prompt Engineering a Prompt Engineer

Our meta-prompt, PE2, effectively guides large language models (LLMs) to perform automatic prompt engineering, improving their performance on various datasets and tasks.

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

This paper presents a catalog of prompt engineering techniques in pattern form, enabling efficient output generation and interaction with large language models like ChatGPT, enhancing software development tasks.

A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Prompt engineering enables the adaptation of pre-trained vision-language models to new tasks, enabling easier application in real-world tasks without updating model parameters.

Prompt Engineering in Medical Education

Prompt engineering is crucial for effectively communicating with generative language models in medical education, addressing concerns like accuracy, bias, privacy, and excessive reliance on technology.

Model-Driven Prompt Engineering

Model-driven engineering supports prompt engineering by creating platform-independent, adaptable prompts for generative AI systems, with tool support through a Langium-based Visual Studio Code plugin.

Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

Prompt engineering is a non-intuitive creative skill that requires practice and learning before it can be used effectively for creating AI art with text-to-image generation.

Prompt Engineering For Students of Medicine and Their Teachers

Prompt engineering can revolutionize medical education by improving understanding and deepening understanding in diverse fields like anatomy, physiology, pathology, pharmacology, and clinical skills.

Prompt Engineering for Healthcare: Methodologies and Applications

Prompt engineering has significantly contributed to healthcare NLP applications like question-answering systems, text summarization, and machine translation, with its importance increasing with improved language models.

Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial

Prompt engineering is an emerging skill for medical professionals to improve their interactions with large language models (LLMs) in AI-based tools, enhancing their use of AI-based tools in healthcare.

Large Language Models Are Human-Level Prompt Engineers

Automatic Prompt Engineer (APE) effectively generates and selects natural language instructions for large language models, outperforming previous methods and human-level prompts on 19/24 tasks.

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