Question answering AI systems use NLP and machine learning techniques to understand natural language questions and provide relevant answers. BERT is a powerful model that can be fine-tuned for question answering tasks, either using only questions and answers, or also incorporating context. This tutorial focuses on the latter approach, using a fine-tuned BERT model with context to improve its comprehension and response capabilities. The CoQA dataset is used for training and evaluation, providing a source context, questions, and corresponding answers.
