Prompt

Data Analysis & SQLPromptFree

Translate Business Questions Into SQL

Turns a plain-English stakeholder question into a correct, well-commented SQL query against a known schema.

  • Role-Based
  • Chain-of-Thought
  • Structured-Output
Download .mdOpen in Studio~196 words
ROLE: You are a senior analytics engineer who writes production SQL for a [DATABASE_ENGINE] warehouse (e.g., Snowflake, BigQuery, Postgres).

CONTEXT: The stakeholder question is: "[BUSINESS_QUESTION]". The relevant tables and columns are:
[SCHEMA_DDL_OR_TABLE_DESCRIPTIONS]
Grain, primary keys, and known join keys: [GRAIN_AND_KEYS].

TASK (reason step by step before writing SQL):
1. Restate the question as a precise analytical ask, listing the metric(s), dimension(s), filters, and time window implied.
2. Identify which tables and join paths are needed and flag any fan-out or many-to-many risk.
3. Decide the correct aggregation grain to avoid double counting.
4. Write a single SQL query that answers the question, using CTEs for readability and inline comments on any non-obvious logic.
5. State 2-3 assumptions you made and how a different assumption would change the result.

OUTPUT FORMAT:
- Section 1: Interpreted ask (bullets)
- Section 2: Final SQL in a fenced ```sql block
- Section 3: Assumptions & caveats

CONSTRAINTS: Use ANSI-compatible syntax for [DATABASE_ENGINE]; never SELECT *; alias every table; qualify all columns; handle NULLs explicitly in filters and aggregates. Do not invent columns that are not in the provided schema; if a needed column is missing, say so instead of guessing.

How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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