A process in natural language processing (NLP) where prompts, or input queries or instructions, are carefully crafted to elicit desired responses from language models. Prompt engineering involves designing prompts that guide the model towards generating accurate, relevant, and contextually appropriate outputs for specific tasks or applications. This iterative process may involve adjusting the wording, structure, or formatting of prompts to optimize model performance and mitigate biases or errors. By tailoring prompts to the intended use case and dataset, prompt engineering enhances the effectiveness and reliability of language models in various NLP tasks, including question answering, summarization, and dialogue generation.
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