Tips for better questions

The agent turns your sentence into SQL, so the more your sentence pins down, the less it has to guess. A useful habit: include a measure, a grouping, and a time window.

Be specific#

What the agent extracts from a good question: an action (aggregate, list, compare), a measure (revenue), a dimension (product category), a time window (2026), and — if you ask — a chart. Vague questions force it to pick defaults you may not want.

Templates that reliably work:

• "Show me [measure] by [dimension] for [time period]"
• "Compare [measure] between [segment A] and [segment B]"
• "Top [N] [things] by [measure], [time period]"

Use your own vocabulary#

You don't need table or column names — semantic search maps business terms to the schema, and fuzzy value matching fixes loose spellings ("electronis" finds Electronics). Two habits multiply this:

• Be consistent. If your team says "bookings", keep saying "bookings" — don't alternate with "revenue" mid-chat.
• Invest in descriptions. Terms resolve through your dataset and column descriptions; the better they are, the more reliably your vocabulary lands. Broader background — metric definitions, business rules — belongs in context documents.

Say the ambiguous parts out loud#

Data has defaults you may not share. Spell them out when they matter:

• Nulls — "treat missing discounts as zero" vs "exclude orders with no discount".
• Dates — "group by week starting Monday", "fiscal year starting in July".
• Similar columns — if the data has revenue, net_revenue, and gross_revenue, say which: "show gross revenue before discounts".
• Units and rounding — "revenue in thousands", "percentages to one decimal".

Ask for the chart you want#

Charts render only when requested. Name the type when you have one in mind — "as a line chart", "as a monthly trend" — or just say "chart" and the agent picks a fit for the data's shape. See Reading results.

Follow up instead of restarting#

The conversation keeps your context — the agent knows what "that" refers to:

1. "Show me revenue by region" →
2. "Now break that down by product category" →
3. "Just the top 5, as a bar chart"

Filters and refinements stack naturally: "only 2026", "exclude the West region", "same thing but for units sold". Two caveats: conversation memory is bounded (roughly the last twenty messages), and it's chat that remembers — not the playground's Ask AI bar, where every prompt stands alone. When you change topics, start a new chat.

When the agent says it can't#

A plain-prose "I can't answer that from this data" is the agent being honest rather than inventing numbers. Useful responses:

And when an answer looks wrong rather than missing: open Show steps, read the SQL, and check the assumption it made — then re-ask with that assumption corrected. Reading the generated SQL is also the fastest way to learn how your own data is structured.