Ask Your Database in Plain English: Natural Language SQL
How LLMs convert business questions into SQL queries — and why it matters
At a Glance
Updated 94 days agoNatural Language to SQL lets business users query databases by asking questions in plain English. Modern LLMs achieve 85%+ accuracy on common business queries, making data accessible without SQL skills.
The Data Access Problem
In most companies, data lives in databases — but only a small group knows how to query it. When marketing needs "which product sold best last month?", they file a ticket and wait 1–2 days. For urgent questions mid-meeting, 2 days is too long.
NL-SQL (Natural Language to SQL) breaks this bottleneck: business users type a question in plain English or Vietnamese, an LLM generates SQL automatically and returns results instantly — no SQL skills required, no waiting.
How It Works
The NL-SQL pipeline has three core steps:
Step 1 — Send the question + schema to the LLM
The LLM needs to understand your database structure to generate accurate SQL. You pass in the schema (table names, column names, data types) along with the user's question.
Step 2 — LLM generates the SQL
The model parses the question's intent, maps it to the right tables and columns, and returns a complete SELECT statement.
Step 3 — Execute on a read-only replica
The system runs SQL against a read-only copy of your production database — ensuring no impact on live data.
async function nlToSql(question: string, schema: string) {
const res = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: `SQL expert. Schema:\n${schema}\nReturn SELECT only.` },
{ role: 'user', content: question }
],
temperature: 0,
});
return res.choices[0].message.content;
}
Note temperature: 0 — SQL needs determinism, not creativity.
Safety Guardrails
Letting users generate SQL without controls is a significant risk. Four essential safety layers:
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