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This paper presents a study on the integration of domain-specific knowledge in promptengineering to enhance the performance of large language models (LLMs) in scientific domains. The proposed domain-knowledge embedded promptengineering method.
The Rise of Deepfakes and Automated PromptEngineering: Navigating the Future of AI In this podcast recap with Dr. Julie Wall of the University of West London, we discuss two big topics in generative AI: deepfakes and automated promptedengineering.
Instead the focus was on what the above-mentioned report called Information Gathering and Sensemaking (eg, using textanalytics to analyse stuff) and Business Uses (eg, finding potential advertisers). Obviously this kind of thing is still very important, but nice to see that the NLG usage is now the most common!
offers a Prompt Lab, where users can interact with different prompts using promptengineering on generative AI models for both zero-shot prompting and few-shot prompting. Automated development: Automates data preparation, model development, feature engineering and hyperparameter optimization using AutoAI.
As large language models, generative AI, and promptengineering have all taken center stage in the AI domain, the interests, demands, and skills required to forge ahead with one’s career have also changed. Traditionally, our NLP track has focused on the usual aspects of NLP, such as textanalytics and sentiment analysis.
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