Power of LLMs have become the new buzz in the AI community. Early adopters have swarmed to the different generative AI solutions like GPT 3.5, GPT 4, and BARD for different use cases. They have been used for question and answering tasks, creative text writing, and critical analysis. Since these models are trained on tasks like next-sentence prediction on a large variety of corpora, they are expected to be great at text generation.
The robust transformer-based neutral networks allow the model to also adapt to language-based machine learning tasks like classification, translation, prediction, and entity recognition. Hence, it has become easy for data scientists to leverage generative AI platforms for more practical and industrial language-based ML use cases by giving the appropriate instructions. In this article, we aim to show how simple it is to use generative LLMs for prevalent language-based ML tasks using prompting and critically analyze the benefits and limitations of zero-shot and few-shot prompting.