{"id":36812,"date":"2022-07-08T09:57:06","date_gmt":"2022-07-08T07:57:06","guid":{"rendered":"https:\/\/www.inovex.de\/?p=36812"},"modified":"2024-05-14T07:21:13","modified_gmt":"2024-05-14T05:21:13","slug":"prompt-engineering-guide","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/prompt-engineering-guide\/","title":{"rendered":"Prompt Engineering and Zero-Shot\/Few-Shot Learning [Guide]"},"content":{"rendered":"

In this blog post, I will give you an overview of prompt engineering, talk about its fascinating capabilities, the definition of zero-shot and few-shot learning, and provide a practical guide on how to adopt prompt engineering for your task of interest.<\/span><\/p>\n

Prompt engineering or prompt learning is a novel approach for leveraging pre-trained language models (LMs) to perform NLP tasks without fine-tuning. In this approach, the model is informed about the target task directly through a natural language task description which is integrated into the actual input sentence in some way. The task description is called a <\/span>prompt<\/span><\/i> as it prompts the model to perform a specific task. Prompt engineering is often implemented in a zero-shot or few-shot setting, which means no or only a few labeled examples are used.<\/span><\/p>\n

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