INNOVATIONS IN PROMPT ENGINEERING FOR IMPROVED DATA PROCESSING AND ANALYSIS
Keywords:
Data Processing, Prompt Engineering, AI, ChatGPTAbstract
Designing effective prompts for the aim of getting valuable outputs from artificial intelligence systems is the focus of the emerging field of prompt engineering, which is focused with the design of prompts. The development of artificial intelligence has resulted in the emergence of a demand for trained quick engineers who are capable of maximising interactions between humans and AI, particularly in the field of natural language processing. After conducting research and conducting analysis, the author of this essay comes to the conclusion that India possesses the necessary resources to develop educational programmes and cultivate talent in order to become a global leader in rapid engineering. There is a creative potential in the interactions that occur between computer science, psychology, language, and prompt engineering. Even though it is present on a global scale, businesses are beginning to recognise the potential of artificial intelligence (AI) when it is provided with the appropriate guidance. This has resulted in an expected annual growth of over twenty percent in the need for quick engineering. The country of India is in an excellent position to capitalise on this opportunity because it possesses a large pool of qualified engineers. The development of huge language models is receiving a lot of attention from a lot of people, and they are making significant progress in this area. One of the primary causes is the introduction of OpenAI's ChatGPT models, specifically the GPT3.5-turbo and the GPT-4. A innovative approach to the production of synthetic data and the distillation of knowledge is presented in this article through the utilisation of fast engineering. The three approaches that are the primary focus of our attention are the fundamental, the composite, and the similarity approaches. One of the most common challenges that machine learning applications face is dealing with imbalanced datasets, and the objective of this research is to investigate the capabilities of various algorithms to deal with such datasets. When compared to the entire dataset, not a single one of the strategies that are based on prompts performs adequately in the studies. On the other hand, the method of similarity prompting is superior to the other approaches and has a great deal of growth potential. According to the findings, there appears to be a significant opportunity to enhance these technologies and provide synthetic data that has a greater diversity of variables.
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