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Prompts Natural Language SQL Query Generator

model data_extraction template risk: low

Natural Language SQL Query Generator

This prompt directs the model to generate a single, clean, production-ready SQL query from a plain English user request, using a specified database (PostgreSQL, MySQL, or SQL Serve…

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}

INPUTS

db REQUIRED

Database type such as PostgreSQL, MySQL, or SQL Server

e.g. PostgreSQL

schema

Optional database schema including tables, columns, relationships

e.g. CREATE TABLE users (id INT, name VARCHAR);

prompt REQUIRED

Natural language description of the desired data

e.g. Show total sales by region last month

preferences

Optional preferences like joins vs subqueries, CTE usage, performance hints

e.g. prefer CTEs

REQUIRED CONTEXT

  • database type
  • user request

OPTIONAL CONTEXT

  • schema
  • preferences

ROLES & RULES

  1. Focus exclusively on SQL generation
  2. Prioritize correctness and clarity
  3. Use explicit column selection
  4. Use clear and consistent table aliases
  5. Avoid unnecessary complexity
  6. Output ONLY SQL
  7. No explanations
  8. No comments
  9. No markdown
  10. Avoid SELECT *
  11. Use standard SQL unless the selected database requires otherwise
  12. If schema details are missing, infer reasonable relationships
  13. Make the most practical assumption and continue
  14. Do not ask follow-up questions

EXPECTED OUTPUT

Format
sql
Constraints
  • Output ONLY SQL
  • No explanations
  • No comments
  • No markdown
  • Avoid SELECT *
  • Use standard SQL unless database requires otherwise

SUCCESS CRITERIA

  • Generate a single correct SQL query
  • Ensure clarity and readability
  • Produce production-ready SQL
  • Handle ambiguities by inferring reasonably

FAILURE MODES

  • Including explanations or comments
  • Using SELECT *
  • Adding unnecessary complexity
  • Outputting markdown or non-SQL content
  • Asking follow-up questions

CAVEATS

Dependencies
  • Database type (PostgreSQL, MySQL, or SQL Server)
  • Schema details (tables, columns, relationships)
  • User request in plain English
  • Optional preferences (joins vs subqueries, etc.)
Missing context
  • Exact syntax for filling placeholders (e.g., how to substitute ${db})

QUALITY

OVERALL
0.95
CLARITY
0.92
SPECIFICITY
0.95
REUSABILITY
0.98
COMPLETENESS
0.94

IMPROVEMENT SUGGESTIONS

  • Standardize placeholder syntax to a common format like {{db}} for better compatibility with templating engines.
  • Add a brief example of a filled-in prompt and expected SQL output to illustrate usage.
  • Explicitly list common database-specific syntax differences in the Rules section.

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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