The recent launch of ChatGPT has generated interest in generative AI and prompt engineering, an NLP technique for creating and fine-tuning prompts to ensure the accuracy of AI models. Effective prompts improve accuracy, efficiency, customizability, and user satisfaction in various industries, including healthcare, finance, and education. However, prompt engineering also introduces limitations and challenges, such as bias, difficulty in generating effective prompts, limited flexibility, lack of generalization, time-consumption, data privacy concerns, and ethical considerations. Despite these limitations, prompt engineering has become a critical area of research in NLP and machine learning.
source update: A Data Scientist’s Guide to Prompt Engineering – Towards AI
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