Discovering Embeddings: A Game-Changer for Contextual Awareness
I was reading up on natural language processing (NLP) and came across something called “embeddings.” At first, it sounded pretty complex, but once I understood the basics, I realized just how powerful they are for maintaining context in conversations.
Essentially, embeddings are a way of converting words or phrases into numerical vectors. The magic happens when you can compare these vectors to find similarities, making it easier to understand meaning beyond just keywords. This opened up a whole new approach to how I could make Empirical’s assistant smarter—storing important context, so it’s always ready to respond intelligently to new requests.
I’m still in the early phases of figuring out how to integrate it, but the more I dive in, the more excited I am about the potential of using embeddings for long-term context.
