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Comment by richardw | Hacker News Reader

The processes the people want the sql for are likely filled with algo’s. An exec wants info in a known domain, set up a text to sql system with lots of context and testing to generate queries. If they think they have something good, get an expert to test and productionise it.

“Thank you for your request. Can you walk me through the steps you’d use to do this manually? What things would you watch out for? What kind of number ranges are reasonable? I can propose an algorithm and you tell me if that’s correct. The admins have set up guidelines on how to reason about customer and purchase data. Is the following consistent with your expectations?”

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This is the same fallacy as low-code/no-code. If you have to check a precise algorithm, you’re effectively coding, and you need a language with the same precision as a programming language.

Only if you want a production-ready output. To get execs able to self-feed enough, this works fine. Look, you don’t see value until it’s perfect. Good, other people do. I see your fallacy and raise you a false dichotomy.

The problem I see is how do you verify that the result of your text-to-sql is really what you were asking for, without understanding the SQL (or “the algorithm”)? It boils down to that you have to know what you are doing, and with the present state of art of AI we can’t have confidence in that.

I’m assuming exploratory work from the exec, not something they make decisions with or put into production. If you need something you can trust, you typically need a lot of checks, including multiple humans.

I play a weird part at work near AI. I use it all the time but I’m the first person to warn everyone that it’s absolutely not trustworthy. No matter your prompt, the data, the guidelines built into it, the output is fundamentally flaky. But I use it while knowing that and working around it. Making the process reliable is a big part of my focus, and usually that means minimising the part the LLM plays. Checks and balances live where things are predictable.

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