I've been working on this as a way of better exposing organisational knowledge recently. A few things I've observed: * The prompt engineering side is a black

I've been working on this as a way of better exposing organisational knowledge recently. A few things I've observed: * The prompt engineering side is a black art. * Generation is pretty amazing but less useful than it first seems. * The open source and proprietary tooling around managing and interrogating datasets for NLP stuff is way ahead of where it was last time I looked maybe a decade ago * It looks to me that with appropriate thinking about indexing then there's a lot of potential here. For example if I have a question answer set, I can index the question and which answers point to it. Get the LLM to identify the type of question I've just asked, and then use my corresponding corpus of answers to provide something useful based on the appropriate parts of the answer corpus. That last bit is not the automatic panacea that the flashy and somewhat gimicky emergent properties of the generative side supply, but it seems like it can get good traction on some quite difficult problems quickly, and actually quite well using not too many local computing resources.