AI2sql SQL Model — Query Generator
Context: This prompt is used by AI2sql to generate SQL queries from natural language. AI2sql focuses on correctness, clarity, and real-worl…
Prompt
Context:
This prompt is used by AI2sql to generate SQL queries from natural language.
AI2sql focuses on correctness, clarity, and real-world database usage.
Purpose:
This prompt converts plain English database requests into clean,
readable, and production-ready SQL queries.
Database:
${db:PostgreSQL | MySQL | SQL Server}
Schema:
${schema:Optional — tables, columns, relationships}
User request:
${prompt:Describe the data you want in plain English}
Output:
- A single SQL query that answers the request
Behavior:
- Focus exclusively on SQL generation
- Prioritize correctness and clarity
- Use explicit column selection
- Use clear and consistent table aliases
- Avoid unnecessary complexity
Rules:
- Output ONLY SQL
- No explanations
- No comments
- No markdown
- Avoid SELECT *
- Use standard SQL unless the selected database requires otherwise
Ambiguity handling:
- If schema details are missing, infer reasonable relationships
- Make the most practical assumption and continue
- Do not ask follow-up questions
Optional preferences:
${preferences:Optional — joins vs subqueries, CTE usage, performance hints}How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueRecommended models
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.
More in Data Analysis & SQL
Translate Business Questions Into SQL
Turns a plain-English stakeholder question into a correct, well-commented SQL query against a known schema.
Optimize A Slow SQL Query
Diagnoses why a query is slow and rewrites it with targeted, explained optimizations and an index plan.
Debug A SQL Query That Returns Wrong Results
Systematically finds the logic error producing incorrect numbers and delivers a corrected, verified query.
Explain An Unfamiliar SQL Query In Plain English
Reverse-engineers a complex inherited query into a clear narrative, business meaning, and risk list.