The generative AI world is moving at a rapid pace, making it challenging to keep up with the latest developments. To refresh knowledge on key concepts, guides have been assembled across various topics, including vector search, which is critical for building GenAI applications. Vector databases are specialized storage systems designed to efficiently handle and query high-dimensional vector data, enabling fast and accurate data retrieval. This enables discovery of linked concepts in search responses, rather than solely focusing on keywords. The nearest neighbor algorithm is another piece of the puzzle that produces highly relevant results to prompts, plotting vectors in a multi-dimensional space to find neighboring points that are closest. HNSW provides an efficient way to navigate and search through complex data by organizing it hierarchically and enabling navigable shortcuts, significantly reducing computational resources needed for searches. Astra DB is a leading vector database that offers high performance, power, and reliability for AI applications, making it an attractive option for organizations building GenAI applications. With over 20 years of experience in building large-scale data distribution architectures, William McLane has deep expertise in designing and leveraging tools to connect and unify enterprise data for real-time AI and complex event processing.