Using Generative AI to recall crucial information from vector embeddings is very unreliable, even with the advent of Hybrid semantic searching.
Our focus has been to use a mixture of techniques in order to improve recall rates to a non-human detectable level, meaning, that when a human runs a query, and RAG occurs, the final response contains the crucial content which the user was seeking, such as correct addresses, complete lists of information, Et cetera.
We have discovered that we are able to do this, anecdotally, and will be publishing information around this shortly.