How accurate is text-to-SQL?

Academic benchmarks show 70-85% accuracy on standardized tests. Real-world performance on complex business schemas is often lower (50-70%). Accuracy varies significantly based on query complexity, schema quality, and how well the AI understands the domain.

Can text-to-SQL handle complex queries?

Simple aggregations and filters work reasonably well. Multi-table joins, nested queries, window functions, and complex business logic are more error-prone. The more complex the intended query, the more likely the AI will generate something incorrect.

Why doesn't text-to-SQL work on my database?

Performance depends heavily on schema design. Cryptic table names, ambiguous columns, undocumented relationships, and complex data models make accurate translation difficult. Better schema documentation and semantic layers improve results.

Natural Language to SQL: How Text-to-SQL Works

Natural language to SQL (also called text-to-SQL or NL2SQL) is a technology that translates questions asked in natural language into SQL queries that can be executed against databases. When you ask "What were sales last month?", a text-to-SQL system generates something like:

SELECT SUM(amount)
FROM sales
WHERE sale_date >= '2024-01-01'
AND sale_date < '2024-02-01'

This translation is what powers many conversational analytics tools.

How Text-to-SQL Works

Step 1: Parse the Question

The system breaks down the natural language:

Subject: What is being asked about? ("sales")
Action: What operation? ("sum")
Filter: What constraints? ("last month")
Output: What format? (single value)

Step 2: Schema Mapping

Map natural language terms to database elements:

"Sales" → sales table
"Amount" → amount column (guessed from context)
"Last month" → date calculation

Step 3: Query Construction

Build SQL that implements the intent:

SELECT clause with appropriate aggregation
FROM clause with correct tables
WHERE clause with filters
JOINs if multiple tables needed

Step 4: Validation

Some systems validate the generated query:

Is the SQL syntactically correct?
Do the referenced tables/columns exist?
Are the joins valid?

Step 5: Execution and Response

Execute the SQL and format results for the user.

Why Text-to-SQL Is Hard

Ambiguity

"Show me sales" could mean:

Total sales amount
Number of sales
Sales records
Sales by region
Sales for a specific period

The AI must pick an interpretation.

Schema Complexity

Real databases have:

Hundreds of tables
Unclear naming conventions
Multiple possible join paths
Undocumented business logic

Business Logic

SQL alone doesn't encode business rules:

What's "active" vs "churned"?
How is revenue recognized?
What exclusions apply?

Language Variation

Users ask the same question many ways:

"Revenue last quarter"
"Q4 sales"
"What did we make in the last three months?"
"Show income for Oct-Dec"

Accuracy Challenges

Text-to-SQL accuracy is measured on benchmarks like Spider and WikiSQL. State-of-the-art systems achieve:

60-70% on complex multi-table queries
75-85% on simpler single-table queries
Lower on real-world business schemas

Even 80% accuracy means 1 in 5 queries is wrong - often without obvious indication.

The Semantic Alternative

Instead of generating SQL from scratch, semantic approaches:

Map questions to semantic concepts: "Revenue" → defined Revenue metric
Query the semantic layer: Use the metric's certified definition
Let the semantic layer generate SQL: Correct SQL is determined by the metric definition

This approach achieves 95%+ accuracy for supported queries because the AI isn't guessing - it's using explicit definitions.

When Text-to-SQL Works

Simple schemas with clear naming
Single-table queries
Basic aggregations and filters
Users who can validate results

When Text-to-SQL Struggles

Complex business schemas
Multi-table joins
Business logic and rules
High-stakes decisions
Users who can't validate

For trustworthy analytics, semantic approaches are generally preferable to raw text-to-SQL.