AI training is resource-intensive. It takes massive datasets, advanced algorithms, and specialized hardware (read: costly GPUs) to teach a model how to understand language, images, or audio. But even after that training is complete, running inferences (the process of generating outputs) can be equally taxing. Querying an LLM in real time for every user request isn’t just expensive, it’s inefficient. Especially when the answer might already exist. To solve this challenge, development teams turned to vector databases. Unlike traditional databases that rely on exact keyword matches, vector databases store information as high-dimensional embeddings like numerical representations of data like text, images, or sound. This makes it possible to perform semantic searches. So instead of looking for exact words, you’re looking for meaning.…