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> what was the average rent in Los Angeles in May 2023? This is actually an example of why I don't think LLM SQL generators are actually going to be that valua
by tqi 3y ago
> what was the average rent in Los Angeles in May 2023?
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.)
- saigal 3y agoThis is a good point and one that we’ve grappled with a lot. My point of view after working on 30+ company deployments is that it depends on the use case. Here’s an example: you have a very analytical BizOps lead at a mid market company. He/she is smart, knows data, can do pivot tables, the whole shabang. But every time they need to get data that isn’t in the BI tool they issue a jira ticket for the data analyst to process / fetch because they don’t know SQL well enough. The dataherald engine is perfect. They are able to use natural language, admittedly with a few iterations, to get the cut of data that they seek. This cuts down hours, or even days from their inquiry.