This is actually an example of why I don't think LLM SQL generators are actually going to be that valuable to most companies. The biggest hurdle for people to get "value" from their data is rarely the SQL part - it's actually knowing what the question is trying to answer. When someone asks that question, do they mean the average rent of new listings from that month? Rent paid by all renters? All sizes and building types? These are all "right" answers, just to very different questions. And no amount of data dictionaries, dbt models, or other context can help narrow that down.
(Not trying to take anything away from this team, who have built a pretty well made product. I just think their approach doesn't quite get to the underlying business problem.)