Prompt engineering is a technique in natural language processing (NLP) aimed at improving the performance of language models by fine-tuning pre-trained models through the careful adjustment of prompts or input text. This method optimizes tasks such as text generation, translation, and question-answering by enhancing the quality of prompts to better align with desired outputs. It is particularly beneficial for NLP practitioners and researchers seeking to increase the accuracy and relevance of language model responses, enabling more effective and contextually appropriate interactions.
