yeah i work on this at https://groundedai.company/

we build a product called Veracity focused on citation and claim checking. legal is a major focus for us, also scientific and technical publishing, where we detect hallucinated citations in submitted works and all that jazz

planning to release something soon focused on the LLM harness layer

we've been working on the problem for years. gradually getting traction

all completely closed source atm, though we often revisit that discussion

i would contest a sibling comment - the solution is not RAG, at least not entirely. 1) because it doesn't necessarily involve 'generation' (e.g our grounding model doesn't depend on gen ai) and 2) RAG is just enriching the context. even if you can get the retrieval right (not a given) it doesn't guarentee that the LLM will faithfully or correctly use that context, or that all its claims are supported by sources. RAG is not the same as traceability and verification

I'm working on something around this that I'm hopeful I can open source in the next couple of weeks. It's just a research project, so not like a product that can be bought and shipped to prod right away, but I'm excited about it. I think a lot of people are working on this right now, at least in part, with things like memory layers and other approaches to "grounding", or with some of the more recent stuff around certification and governance. But there's sort of a split right now between:

- Groups focused strictly on model capability (i.e. benchmark scores)

- Groups really focused on governance, interpretability, control planes, etc

- A small but excitable minority of weirdos (me included) who think they're the same thing

A lot of the stuff that gets loosely clustered under neurosymbolic methods might be interesting if you're looking to dive in. The sort of pattern you're describing is something that a lot of people have taken stabs at from different directions, or at least have tangentially nipped at while working on related problems.

The name of the solution is RAG - retreival augumented generation. It is pretty easy to whip up your own over a corpus of material. I built one for myself within a week over a corpus of my favorite cook and homebrewing books. All you need is source material, postgress (pgvect) and some compute (llama.cpp with a decent chat model for chunking and labeling, an embedding and a rerank model. or some $ to use those via a router). When the corpus is indexed, you use your favorite coding agent and a system prompt that forces the model to answer only from search results.

Not sure why a solution is even needed. If I had to compile all the resources I had consulted for something like C programming, they wouldn't amount to a single shelf. I would say the same thing for research where the amount of sources is still very low. It's kinda reminded me of the big paper manuals and books before the internet. I don't think the whole library of congress was needed to write UNIX.

So AI being a source of truth and information is not a something that important. It's a generator and we should use it as such, getting it to map domains and repeating patterns. It should not be used as an encyclopedia or oracle.

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