BERT and RankBrain are both components of Google’s search algorithm, and they both utilize AI, but they serve different functions and were developed to handle different types of search queries. RankBrain, introduced in 2015, uses machine learning to interpret complex search queries, especially those that haven’t been seen before. It’s especially good at understanding metaphors, puns, and phrases that have a certain implied context or that could be interpreted in different ways. On the other hand, BERT, introduced in 2019, focuses on understanding the context of words in search queries. It’s a neural network-based technique for natural language processing (NLP) pre-training that understands the nuances and context of words in a sentence, thus improving the interpretation of the user’s intent. So, while RankBrain helps Google deal with novel queries, BERT helps Google better understand the context of words in a sentence, ensuring that the nuances of language are better understood. Both components, though different in their operations, ultimately work towards enhancing the relevance and precision of the search results delivered by Google.  They coexist within Google’s search ecosystem, each playing their unique roles to ensure a more effective and accurate understanding of user queries, thereby improving user experience on the search engine. They highlight Google’s commitment to continually evolving and adapting its search technology to better meet users’ needs and the complexity of human language and search behavior.