Docs · Step 4

Ask a question

Plain English in, a checked answer out, with the query and rows behind it when there are any.

1. Open Chat

  1. Select the dataset (or datasets) you want to ask about.
  2. Open the Chat page.

2. Ask a question

Type a question the way you'd ask a colleague: "what's the average final grade for students who attended office hours", "how many rows are missing a value in the income column", "what predicts whether a patient was readmitted". No query language, no column-name syntax required.

3. Read the answer

CLAIR answers directly, and marks an answer verified when it came from a query CLAIR actually wrote and ran against your table (or a saved model, or a rule-based computation), rather than from the language model alone. A verified answer shows the query and the rows it ran against; click through to open both.

An unmarked answer isn't necessarily wrong

It just means CLAIR couldn't check it deterministically, most often a genuinely open-ended or exploratory question. Treat it the way you would a knowledgeable colleague's answer: useful, but worth checking against the data yourself for anything that matters.

Some questions are declined outright rather than answered with a guess: if a question names a value your data doesn't actually hold, or asks about a group CLAIR can't map onto a real column, it says so and names the mismatch instead of returning a confident wrong number.

4. Ask a follow-up

Chat keeps the last question in mind for a natural follow-up ("now break that down by year") without repeating the full context yourself.

What "verified" means, precisely

  • A bare aggregate (a sum, average, count) over a table that also contains a total or rollup row is checked before it's allowed to verify, so it can't silently double-count that row.
  • A question about "how many people" against a dataset with more than one row per person (repeated measurements, say) is checked, and the badge is withheld if the raw row count would answer a different question than the one you asked.
  • A group comparison excludes a rollup label (like an "All Regions" summary row) from the statistical test it runs, rather than treating it as a real group.