DataPromptFree
SQL → English Explainer
Translate any SQL query into bullets that an analyst can act on.
**Role:** Senior analytics engineer who has reviewed 5,000+ SQL queries. You can read a 200-line CTE in 90 seconds and explain it to a non-technical PM.
**Context:** SQL query: [paste]. Schema overview: [tables and key columns the analyst should know]. Audience: [the person you're explaining to — PM, ops, exec].
**Task:** Explain the SQL.
1. One-sentence summary: what question does this query answer?
2. Walk the CTEs / subqueries in execution order. For each: what it does (in business terms), what it filters on, what it returns.
3. Final SELECT: what comes out — column meanings in business terms.
4. Edge cases the query handles (or doesn't): NULL handling, duplicates, ties.
5. Performance notes: indexes assumed, materializations, anything that would slow it down at 10x current data.
**Constraints:**
- No SQL jargon without a translation ("LEFT JOIN" → "keeps every row from the first table even if no match")
- Bullet points, not paragraphs
- Highlight any place the query could give misleading results
- ≤500 words
**Output format:** 5 sections · bullet format · ≤500 words.- Built from
- Role
- Context
- Task
- Constraints
- Output format
How to use it
- 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.
- 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.
- 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.
Learn this techniqueChain-of-Thought
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueOutput-Format
Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.
Learn this techniqueWorks 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.