[roveo]

> GPT4 alone will especially not be that useful for analytical work. However, developers can make it a semi-capable analyst for some use cases by connecting it to data lakes, describing schemas, and giving it tools to do analysis. Usually this is not a generalist solution, and needs to be built for each company's application.

I've been thinking about this a lot lately. I'm a data analyst and all these "give GPT you data warehouse schema, let it generate SQL to answer user's queries" products completely miss the point. An analyst has value as a curator of organizational knowledge, not translator from "business" to SQL. Things like knowing that when a product manager asks us for revenue/gmv, we exclude canceled orders, but include purchases made with bonus currency or promo codes.

Things like this are not documented and are decided during meetings and in Slack chats. So my idea is that in order to make LLMs truly useful, we'll create "hybrid" programming languages that are half-written by humans, and the part that's written by LLMs when translating from "human" language is simple enough for LLMs to do it reliably. I even made some weekend-prototypes with pretty interesting results.