Sora Prompt to Generate SQL Queries

Sora, developed by OpenAI, is primarily known as a video generation model, but its integration into the OpenAI ecosystem also allows leveraging its textual capabilities for technical tasks like generating SQL queries. By crafting precise and structured prompts, you can obtain optimized SQL queries tailored to your database schema and business needs. Whether you work with MySQL, PostgreSQL, SQL Server, or SQLite, a well-constructed prompt enables you to generate complex queries including joins, subqueries, aggregations, and window functions. The major advantage lies in the model's ability to understand a natural language description and transform it into a syntactically correct and efficient query. This approach is particularly useful for developers wanting to speed up their workflow, data analysts not fully proficient in SQL, or teams needing to quickly prototype queries on complex schemas. In this guide, we offer optimized prompts to get the most out of Sora for SQL query generation, with variants adapted to each expertise level.

The prompt

Sora

You are an expert in relational databases and SQL optimization. I work with a [MySQL/PostgreSQL/SQL Server] database with the following schema:

[Paste your table schema with columns, types, and relationships]

Generate a SQL query to answer the following need: [Describe your need in natural language, e.g., "Get monthly revenue by product category for the year 2025, sorted by descending month"]

Constraints:

Use explicit aliases for every table and computed column
Add SQL comments explaining each section of the query
Optimize the query for performance (suggested indexes if relevant)
Handle NULL values appropriately
Follow SQL best practices (no SELECT *, explicit joins)

Provide the final query, then a step-by-step explanation of its logic.

Personalize this prompt with Léa

Answer 3 questions and Léa tailors the prompt to your situation.

Why it works

This prompt works due to the expert role assignment that guides the model towards professional-quality responses. The structure in distinct sections (schema, need, constraints) allows the model to process each aspect methodically. The explicit constraints on SQL best practices ensure a production-ready result, not just syntactically correct.

Expected result

You will obtain a complete, commented, and optimized SQL query, accompanied by a detailed explanation of each clause. The query will follow professional naming conventions with clear aliases, explicit joins, and proper handling of edge cases like NULL values.

Variants by level

FAQ

How do I effectively provide my database schema in the prompt?

The most effective way is to paste the DDL (CREATE TABLE) directly into the prompt, including column types, primary keys, foreign keys, and existing indexes. If your schema is too large, focus on the tables involved in your query and mention the relationships between them. You can also use a simplified notation like: users(id PK, name, email, created_at) → orders(id PK, user_id FK→users, total, status, order_date).

Are the generated queries directly usable in production?

Generated queries are an excellent starting point, but they should always be tested and validated before production deployment. Check the syntax specific to your DBMS, test with EXPLAIN ANALYZE to validate the execution plan, and ensure performance is acceptable on your actual data volume. The model does not know your real data or value distribution, which can impact optimization choices.

How do I get queries adapted to a specific DBMS?

Always specify your database management system and its version in the prompt (e.g., "PostgreSQL 16" or "MySQL 8.0"). Each DBMS has its syntactic peculiarities: PostgreSQL supports recursive CTEs and advanced window functions, MySQL has different syntax for LIMIT and date functions, and SQL Server uses TOP instead of LIMIT. By specifying your DBMS, the model will adapt syntax, native functions, and optimizations specific to your environment.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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