Data Analysis & SQL5.0 · 0 ratings

Translate Business Questions Into SQL

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

Role-BasedChain-of-ThoughtStructured-Output

Prompt

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 this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer 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.

Learn this technique
Chain-of-Thought

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

Learn this technique
Structured Output

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

Learn this technique

Recommended models

claudegpt-4ogemini

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

More in Data Analysis & SQL

Optimize A Slow SQL Query

Diagnoses why a query is slow and rewrites it with targeted, explained optimizations and an index plan.

Read prompt

Debug A SQL Query That Returns Wrong Results

Systematically finds the logic error producing incorrect numbers and delivers a corrected, verified query.

Read prompt

Explain An Unfamiliar SQL Query In Plain English

Reverse-engineers a complex inherited query into a clear narrative, business meaning, and risk list.

Read prompt

Design A Star Schema For Analytics

Produces a dimensional model with fact and dimension tables, grain, keys, and SCD strategy for a reporting need.

Read prompt