Prompt Engineering
Prompt engineering is the art and science of crafting effective instructions—prompts—to guide large language models (LLMs) towards desired outputs. Current research focuses on developing automated methods for prompt optimization, exploring techniques like chain-of-thought prompting, and adapting prompts to specific LLMs and tasks (e.g., code generation, question answering, medical image analysis). This field is significant because effective prompt engineering dramatically improves the accuracy, efficiency, and reliability of LLMs across diverse applications, ranging from healthcare and education to software development and scientific research.
Papers
Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts
Sui He
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
Jenish Maharjan, Anurag Garikipati, Navan Preet Singh, Leo Cyrus, Mayank Sharma, Madalina Ciobanu, Gina Barnes, Rahul Thapa, Qingqing Mao, Ritankar Das
Language Agents as Optimizable Graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Ming Wang, Yuanzhong Liu, Xiaoming Zhang, Songlian Li, Yijie Huang, Chi Zhang, Daling Wang, Shi Feng, Jigang Li
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
Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
Elad Levi, Eli Brosh, Matan Friedmann
Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
Aryan Agrawal