How to Turn Plain-English Requirements into SQL You Can Actually Trust

"Just write me a query for X" is one of the most common requests thrown at whoever's closest to the database — and one of the easiest to get subtly wrong. The SQL runs, returns rows, looks fine... and is quietly answering a slightly different question than the one that was asked.

Where plain-English-to-SQL translation actually breaks

Ambiguous joins. "Show me customers and their orders" doesn't say whether customers with zero orders should be included. That's the difference between an INNER JOIN and a LEFT JOIN, and it changes the result set, not just the syntax.

Unstated assumptions about NULLs. "Get all users without a phone number" — does an empty string count? A generated query that only checks IS NULL will silently miss empty-string rows if that's how the data actually got stored.

Self-referential relationships. Anything with a manager/employee, parent/child, or category/subcategory structure trips up naive query generation constantly, because the same table plays two roles in the same query (an employee row and its manager's row are both in the employees table).

Dialect differences. LIMIT vs TOP vs ROWNUM, date function names, string concatenation operators — a query written for Postgres in mind doesn't always run as-is on SQL Server or Oracle, and a generated query that doesn't declare its target dialect is a query you can't fully trust yet.

A checklist before you run a generated query

Read the JOIN types out loud. Does "customers and their orders" actually mean "only customers who have orders," or all of them? Check that the join type matches what was actually asked.
Check what happens to NULLs and empty values in every WHERE clause — they're the most common source of "technically correct, actually wrong" results.
Confirm the dialect the query targets matches where you're actually running it.
Ask for the assumptions, not just the query. A generator that states its assumptions ("treating 'recent' as the last 30 days") is far more useful than one that just hands you SQL and lets you find out the hard way.

Where an ER diagram earns its keep

For anything with more than two or three tables, seeing the relationships visually catches mistakes that reading raw SQL doesn't. A self-referential foreign key is obvious in a diagram (the table has an arrow pointing back to itself) and easy to miss buried in a WHERE clause.

Frequently asked questions

Do I have to provide a schema for an accurate query?
No, but it helps a lot. Without one, a generator has to infer table and column names from your description; with one, it can match your real tables instead of guessing.

What SQL dialect should I expect back?
Most tools default to a standard, Postgres-like dialect unless told otherwise — always check which dialect a generated query targets before running it against MySQL, SQLite, or another engine, since LIMIT/TOP/ROWNUM and date functions all differ.

What should I do if a generated query looks wrong?
Read the JOIN types out loud against your original request, check how NULLs and empty strings are handled in every WHERE clause, and if it's a complex multi-table request, try splitting it into smaller pieces rather than trusting one large generated query blindly.

Craftloop's SQL Generator does exactly this: describe what you need in plain English (with an optional schema and key constraints), and get back the query, the dialect it targets, the assumptions it had to make, a plain-English explanation of what it does, and an optional ER diagram of the tables involved — so you can check its work instead of just trusting it. Free, no sign-in required.