AI prompts for data analysis: SQL, trends, and interpretation

Prompt templates for data analysis tasks — generating SQL queries, interpreting results, spotting trends, and cleaning messy datasets.

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Why data analysis prompts fail

No schema provided: the model invents column names and table structures it doesn't have.
Underspecified goal: 'analyze this data' can mean dozens of things — specify what decision the analysis should support.
Missing constraints: no row limits, no date ranges, no filter criteria — the model guesses.
No output format: data analysis results need a specific format (table, SQL query, bullet summary) or they become walls of text.
No validation request: SQL can look correct and still have logic errors — always ask for a check step.

Parameters that make data prompts reliable

Always provide the schema: table name, column names, data types, and a 2-3 row sample.
State the decision: 'The output will be used to decide [X]' gives the model the right level of detail to target.
Specify output format: query only, table, or bullet list with key numbers.
Ask for a logic check: 'Explain what the query does and flag any assumptions you made about the data.'
For trend analysis: specify the time grain (daily/weekly/monthly) and the metric definition explicitly.

Templates

SQL query from plain-English requirements

Write a SQL query for the following requirement.

Database: [PostgreSQL / MySQL / SQLite — specify]

Table schema:
Table name: [name]
Columns: [column_name (type), column_name (type), ...]
Sample rows (2-3):
[paste 2-3 rows]

Requirement:
[describe what the query should return in plain English]

Constraints:
- Filter to: [date range / status / category — specify or remove]
- Limit results to: [number] rows if no specific filter
- Sort by: [column and direction]

Output:
1. The SQL query.
2. A plain-English explanation of what it does (2-3 sentences).
3. Assumptions you made about the data.
4. Any edge cases this query does not handle.

Opens on home with the prompt prefilled.

Open in Promptea

Trend analysis and interpretation

Analyze the data below and identify meaningful trends.

Metric: [what is being measured]
Time grain: [daily / weekly / monthly]
Period: [start date] to [end date]
Decision this analysis supports: [one sentence on what you will decide with this]

Data:
[paste data as CSV, table, or bullet list]

Output:
1. The main trend in 1-2 sentences (direction, magnitude, notable changes).
2. Up to 3 specific observations worth investigating (not generic statements — name the exact time period or data point).
3. What the data does NOT tell you (limitations, missing context).
4. One follow-up question to answer before acting on this data.

Constraints:
- Do not speculate about causes unless the data explicitly supports it.
- If a trend has contradictory signals, note the contradiction instead of picking one.
- Keep the total response under 250 words.

Opens on home with the prompt prefilled.

Open in Promptea

FAQ

Which AI model is best for SQL generation?

For SQL generation, GPT and Claude are both strong. GPT tends to write cleaner queries quickly; Claude handles complex multi-step logic well and is more likely to flag ambiguities in the schema. For data interpretation, Claude's tendency to note uncertainty is useful — it will flag when the data doesn't support a strong conclusion instead of speculating.

How do I avoid hallucinated column names in SQL prompts?

Always paste the exact schema — table name, column names, types, and at least 2 sample rows. The model cannot invent a name it can see in front of it. If you add the constraint 'Use only the column names listed in the schema above — do not invent new ones,' most models will respect it. For critical queries, ask it to list all column names it used and verify them against the schema.