The current state of search techniques has been limited by keyword matching, which suffers from semantic loss, making it difficult to capture the true meaning of queries. The advent of natural language understanding via neural networks and large language models (LLMs) has enabled computers to comprehend human language at a high level, leading to advancements in LLM-powered search or Neural Search. This technology uses word vectors, transformers, and BERT to map keywords from human language space to a meaning/concept multi-dimensional space, allowing for more accurate and relevant results. With Neural Search, users can search by the true essence of concepts, removing language barriers and reducing costs associated with building and maintaining search systems.