From e150a3e86c51c3d1b435608a97f8815978165850 Mon Sep 17 00:00:00 2001 From: Juana Blacklock Date: Thu, 20 Mar 2025 10:40:05 +0800 Subject: [PATCH] Add 'Want To Have A More Appealing ChatGPT For Productivity? Read This!' --- ...e-Appealing-ChatGPT-For-Productivity%3F-Read-This%21.md | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 Want-To-Have-A-More-Appealing-ChatGPT-For-Productivity%3F-Read-This%21.md diff --git a/Want-To-Have-A-More-Appealing-ChatGPT-For-Productivity%3F-Read-This%21.md b/Want-To-Have-A-More-Appealing-ChatGPT-For-Productivity%3F-Read-This%21.md new file mode 100644 index 0000000..0cc2f94 --- /dev/null +++ b/Want-To-Have-A-More-Appealing-ChatGPT-For-Productivity%3F-Read-This%21.md @@ -0,0 +1,7 @@ +Abstract + +Prompt engineering, the practice of designing and refining prompts to elicit desired responses from language models, has emerged as a critical area of focus in the field of artificial intelligence (AI) and natural language processing (NLP). As large language models (LLMs) continue to evolve, the significance of effective prompt design has grown, shaping the interactions between humans and AI. This article explores the principles of prompt engineering, the methodologies behind effective prompt design, the challenges faced, and best practices to maximize the utility of LLMs across various applications. + +Introduction + +In recent years, LLMs like OpenAI's GPT-3 and similar architectures have revolutionized the way we interact with machines. These models have the capability to generate coherent and contextually relevant text based on the prompts they receive. However, the performance of these models is not solely dependent on their underlying architecture \ No newline at end of file