The intent-utterance model is a widely used paradigm for building Natural Language Understanding (NLU) systems, which structure data into intents, utterances, and entities to enable conversational assistants to recognize user requests and extract relevant information. This model involves training the NLU with examples of user phrases and fine-tuning it using a training dataset that includes entities, synonyms, and built-in entity types. The output of an NLU provides a confidence score for matched intents and can be used by a dialogue manager to determine the next step in the conversation. Two common ways to train an NLU are cloud-based training and local training, with various platforms and frameworks available for customization and deployment.