Prof Murat is calling for examples of how LLMs can help make DBs more usable.
I note that Julius.ai is making data analytics easier - just upload some data and ask for some charts and it does an ok job.
The problem of going from natural language to SQL is still a far from solved problem. The main benchmark for this is Bird-Bench and accuracy is only in the high 60s - https://bird-bench.github.io/
I have been working on making tables queryable using function calling, where you a fixed number of canned queries based on the primary key and an event-time column. PyData talk on this - https://www.youtube.com/watch?v=dRcjTe5qgwM
I note that Julius.ai is making data analytics easier - just upload some data and ask for some charts and it does an ok job.
The problem of going from natural language to SQL is still a far from solved problem. The main benchmark for this is Bird-Bench and accuracy is only in the high 60s - https://bird-bench.github.io/
I have been working on making tables queryable using function calling, where you a fixed number of canned queries based on the primary key and an event-time column. PyData talk on this - https://www.youtube.com/watch?v=dRcjTe5qgwM