Everyone has questions; few write SQL
The bottleneck in most analytics teams isn't insight—it's the queue of people waiting for someone to run a query. LLMs that translate plain English into SQL can shorten that queue.
Making it trustworthy
- Constrain it to a well-modelled, documented schema.
- Show the generated SQL so users can sanity-check it.
- Validate against known metrics before trusting answers.
- Log questions to learn what people actually need.
A helper, not a replacement
Natural-language querying is great for self-serve exploration. For numbers that drive big decisions, a human-reviewed, governed metric still wins.
