alt Hacker NewsTrust & explainability is the biggest issue here.
We've been building natural language analytics at Veezoo (https://www.veezoo.com/) for 10 years, and what we find is that straight Text-to-SQL doesn't scale. If AI writes SQL directly, you're building on a probabilistic foundation. When a CFO asks for revenue the number can't just be correct 99% of times. Also you can't get the CFO to read SQL to verify.
We're solving that with an abstraction layer (Knowledge Graph) in between. AI translates natural language to a semantic query language, which then compiles to SQL deterministically.
At the same time you can translate the semantic query deterministically back into an explanation for the business user, so they can easily verify if the result matches their intent.
Business logic lives in the Knowledge Graph and the compiler ensures every query adheres to it 100%, every time. No AI is involved in that step.
Veezoo Architecture: https://docs.veezoo.com/veezoo/architecture-overview
Replies
Don't you still need to unit test and version control the SQL artefact that is produced? You need to be able to see which query was used on which date and how it was validated.
(Prompts need to be version controlled too, of course)
Thanks for sharing the links, the architectural overview is very insightful.
I'm curious how this approach manages cardinality explosion? Also, how do you handle cases where a user asks for data that requires running multiple queries, specifically where each query depends on the results of the previous one?