If you've adopted Loki for log aggregation, you've probably had this moment: you need to find something in your logs right now, you open Grafana, and you stare at the empty LogQL query bar trying to remember whether it's |= or =~ for the substring filter. Five minutes later you've cobbled something together, run it, gotten zero results, and you're not sure if the query is wrong or the logs aren't there.
This is the kind of friction AI is good at removing. LogQL has a small, structured grammar; the model knows it; you describe what you want; you get a working query. But — and this is the recurring theme — the model will also sometimes produce queries that are syntactically valid and semantically wrong, and there's a specific way to catch that.
The basics AI handles well
These are the patterns I use AI for daily without much verification:
Label filter + substring:
