How AI Turns Your Question Into SQL

You type 'show me top customers.' LILA returns a perfect SQL query. Here's what happens in between, explained without the jargon.

aisqltechnicalexplainernatural-language

The Magic Trick

Natural language to SQL looks like magic. You type: “Show me customers who ordered more than $500 last month.”

LILA returns:

SELECT c.name, c.email, SUM(o.total) as total_spent
FROM customers c
JOIN orders o ON c.id = o.customer_id
WHERE o.created_at >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')
  AND o.created_at < DATE_TRUNC('month', CURRENT_DATE)
GROUP BY c.id, c.name, c.email
HAVING SUM(o.total) > 500
ORDER BY total_spent DESC;

How does that happen?

Step 1: Understanding Your Database

Before any question gets answered, LILA learns your database structure.

When you connect:

  • Table names are cataloged (customers, orders, products)
  • Column names and types are recorded (name: text, total: decimal)
  • Relationships are mapped (orders.customer_id → customers.id)
  • Primary and foreign keys noted

This becomes a schema map. The AI references it for every query.

Step 2: Adding Business Context

Schema tells you structure. Context tells you meaning.

You add definitions like:

  • “Revenue” means SUM(order_total) - SUM(discount_amount)
  • “Active customer” means orders within the last 90 days
  • “This month” starts on the 1st, not rolling 30 days

This context layer translates your terminology into database terms.

Step 3: Processing Your Question

When you ask a question, the AI does several things:

Intent recognition. What kind of answer do you want? A count? A list? A comparison?

Entity extraction. Which tables and columns are relevant? “Customers” → customers table. “$500” → order total filter.

Relationship mapping. How do these tables connect? Customers to orders via customer_id.

Constraint interpretation. “Last month” → date range. “More than $500” → HAVING clause.

Step 4: Generating SQL

With all context gathered, the AI constructs SQL:

  1. SELECT appropriate columns based on what you’re asking for
  2. FROM the relevant primary table
  3. JOIN related tables using known relationships
  4. WHERE conditions match your constraints
  5. GROUP BY for aggregations
  6. HAVING for aggregate filters
  7. ORDER BY for sorting

The output is standard SQL that runs on PostgreSQL or MySQL.

Step 5: Self-Healing

Sometimes the first attempt fails. Maybe a column name was wrong. Maybe the join logic was off.

When an error occurs:

  1. AI reads the database error message
  2. Identifies what went wrong
  3. Generates a corrected query
  4. Tries again

This happens automatically, usually invisibly. You see results, not errors.

What Makes This Accurate

Schema awareness. The AI only references columns that exist. No hallucinated field names.

Context injection. Your business definitions are included in every query generation prompt.

Validation. SQL is validated before execution. Syntax errors caught before they hit your database.

Learning from corrections. When you correct the AI, those corrections influence future queries.

What Makes This Challenging

Natural language is ambiguous. “Last month” could mean:

  • The previous calendar month
  • The last 30 days
  • Since the 1st of last month to now

“Revenue” could mean:

  • Gross sales
  • Net after discounts
  • Net after refunds

That’s why business context matters so much. Without your definitions, the AI guesses. With your definitions, it knows.

The Accuracy Question

How accurate is LILA’s natural language to SQL?

With proper business context: 85-90% first-attempt accuracy.

Without context: 60-70% accuracy.

The self-healing mechanism catches most of the remainder. Final accuracy typically exceeds 95%. Your database credentials stay secure throughout.

What It Can’t Do (Yet)

Current limitations:

Cross-database queries. One database connection per query. No joining across systems.

Write operations. SELECT only by default. INSERT/UPDATE available but restricted.

Complex business logic. Multi-step calculations sometimes require breaking into multiple questions.

Ambiguity resolution. When the question is genuinely unclear, the AI picks an interpretation rather than asking for clarification.

Under the Hood

The actual AI is a large language model (LLM) fine-tuned for SQL generation.

It receives:

  • Your schema (tables, columns, relationships)
  • Your business context (definitions, rules)
  • Your question
  • Instructions for SQL generation

It returns:

  • SQL query
  • Explanation of what the query does
  • Confidence indicators

Different models handle different complexity levels. Simple queries use fast, lightweight models. Complex analytics might use more capable (slower) models.

Why This Works Now

Natural language to SQL isn’t new. But previous approaches required:

  • Rigid question formats
  • Extensive training per database
  • Low accuracy on real queries

Modern LLMs changed this:

  • Flexible question understanding
  • Minimal per-database setup
  • High accuracy out of the box

The technology finally works well enough for production use. That’s why we built LILA on this foundation.

Want to see natural language to SQL in action? Try LILA free and query your database without writing SQL.