Training a 4B model to produce 81% faster query plans than Postgres
Thread
Unofficial Hacker News client; not affiliated with Y Combinator.
Training a 4B model to produce 81% faster query plans than Postgres
Unofficial Hacker News client; not affiliated with Y Combinator.
2001zhaozhao · · focus · HN ↗
Infra: "Hmm, let's check... Well would you look at that, it seems like your LLM query planner usually works and produces fast queries, but this time when you changed a variable name to trigger query rebuild, it happened to hallucinate and miss an index, would you mind re-running the LLM a few times until you get a faster query?"
malisper · · focus · HN ↗
Tanjreeve · · focus · HN ↗
egeozcan · · focus · HN ↗
By people with a specific skill set. LLMs generation can also be fixed and verified by people with a certain skill set, and non-deterministic computing doesn't automatically mean unpredictable. When people say that the LLMs are a black box, it means unpredictability in unknown situations.
You do structured output, input validation, output validation, lower temperature, limit decisions, RL, etc. to increase predictability to near certainty. It's just statistics after all. Or you can as well generate the code to do the job.
It's just that the required skill set is a different one to do those things, and unusual in the context of DB administration.
Tanjreeve · · focus · HN ↗
egeozcan · · focus · HN ↗
I just find the "all llms are non dererministic and therefore unreliable" narrative a bit backwards. All software that has more than 0 users needs to deal with non-determinism anyway :)