Comment by yencabulator
2 years ago
I think you'll find that INSERT and UPDATE are very much commands. SQL queries are outcome-driven sure, but try to move beyond pure queries and outcome-driven computing, without some sort of machine learning, gets quite difficult. And moving outside of a single SELECT is a huge barrier.
Even within the scope of SQL, consider an ML system that can slice-and-dice previous SQL queries interactively, based on non-expert user input.
Consider an ML system that essentially edits an proposed SQL transaction as a whole, based on your requests. Previewing results etc, adjusting INSERTs and UPDATEs as user clarifies intent. User terminology focuses on the outcome, not on the individual commands, ordering, etc.
Now move from that narrow domain into something like "I want to organize a conference", "I want to write a book", etc, and all the things that are beyond a single SQL SELECT.
I built a system that uses GPT to write KQL queries (similar to SQL) for a specific table. It could even combine multiple queries or throw in a custom chart if requested.
OpenAI's models are good at writing SQL. I think they finally allow the type of use case that SQL itself was supposed to provide as originally envisioned.