Natural language understanding has evolved significantly since the development of GPT-3, with transformer language models like large language models (LLMs) gaining prominence. However, natural language understanding units (NLUs) still offer advantages in terms of control and performance, particularly when it comes to fine-tuning for uncommon terminology, reducing inference costs and latency, and maintaining data sovereignty. On the other hand, LLMs excel at emulating NLU behavior with greater accuracy, enabling free-form behavior like open-ended entities and producing accurate results without retraining. Ultimately, the choice between NLUs and LLMs depends on specific use cases and requirements, highlighting the importance of conducting due diligence to determine the most suitable technology for each application.