You know, aside from this being a blatant feature-length advertisement for what they're selling, I almost thought this was a clever idea.
I thought it involved prompting the LLM to write SQL code to query a knowledge base of documents, and index into them, so that you'd know where to look in the original documents for your authoritative answer. So it would be a meta-search agent.
But apparently, they intend the queried documents to feed back into training the LLM? That's just gasoline on a dumpster fire.
I cannot figure out why LLMs are relevant to their solution. This whole thing comes down to a similarity search via vectors.
The LLM layer seems completely unnecessary. Why do you have a schema that requires an LLM to decide which column to query (which is the LLM's only unique value in this proposal)? Why are you not normalizing into a single column?
I thought it involved prompting the LLM to write SQL code to query a knowledge base of documents, and index into them, so that you'd know where to look in the original documents for your authoritative answer. So it would be a meta-search agent.
But apparently, they intend the queried documents to feed back into training the LLM? That's just gasoline on a dumpster fire.