In my most recent articles and books, I discussed our radically different approach to building enterprise LLMs from scratch, without training, hallucinations, prompt engineering or GPU, while delivering higher accuracy at a much lower cost, safely, at scale and at lightning speed (in-memory). It is also far easier to adapt to specific corpuses and business needs, to fine-tune, and modify, giving you full control over all the components, based on a small number of intuitive parameters and explainable AI. 

Download the full paper, here.

It has links to the sources including deep retrieval PDF parser, real-life input corpus, backend tables, and so on.

This technical paper is much more than a compact version of past documentation.  It highlights new features such as un-stemming to boost exhaustivity, multi-index, relevancy score vectors, multi-level chunking, various multi-token types (some originating from the knowledge graph) and how they are leveraged, as well as pre-assigned multimodal agents. I also discuss the advanced UI — far more than a prompt box — with unaltered concise structured output, suggested keywords for deeper dive, agent or category selection to increase focus, and relevancy scores. Of special interest: simplified, improved architecture, and upgrade to process word associations in large chunks (embeddings) even faster.