Any text-to-SQL benchmark should address difficulties of real-world data stores

Interesting... at my current job, my team and I are solving this problem of how to make sure LLM understand our SQL data warehouse to answer analytical questions for clients. We have a 20-year old database with rotting schema as the blog post describes. So we had to rebuild the database in a way that is well structured and governed. Once that hard work is done, we slap dbt models on top of business metrics with model YAML files (and some common MD files) carrying a lot of semantic and metadata info for them.

Then our software engineering team ingest the dbt models (we have to tactically create dbt models; that is, always think "what would make LLM hallucinate less" as we are implementing them) and info from the semantic layer to build context for the LLM, and use that to answer analytical questions. So far, it's been promising. The accuracy isn't zero like the blog's author suggested though. We have built like 30 metrics in dbt and semantic layer in the last quarter, and asked the research and analytics teams to do internal testing on the LLM app. I will find out how accurate this approach is from the feedback soon.

  > It is widely known that an LLM can only find data it has seen before.

What does this mean? It’s obviously not literally true, so what is the author trying to convey?

Friend of mine built a startup around this, allowing non-techies to "query their database using natural language": https://www.blazesql.com/ Not sure how he achieved it (if TFA is to be believed), but it's my impression that his query generation and results are fairly robust.