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How to Build a Text-to-SQL AI Tool Using Gemini for BigQuery and Modern Databases

Jun 8, 2025 | AI, Databases | 0 comments

If you’re a software engineer or data professional, you know how tedious it can be to write SQL queries for complex databases. With AI-powered Text-to-SQL tools, you can convert natural language questions into executable SQL queries automatically. In this tutorial, we’ll show you how to implement a Text-to-SQL AI tool using Google’s Gemini model, step by step.

This guide covers everything you need to know: architecture, prompt design, schema representation, query validation, and execution.

What Is Text-to-SQL?

Text-to-SQL is an AI-powered system that allows users to input natural language questions and outputs SQL queries that can be executed on a database.

Example:

User Input:
“Show me total sales per region for last month”

Generated SQL:

SELECT region, SUM(sales)
FROM orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH)
GROUP BY region;

Benefits:

  • Saves time writing queries manually
  • Reduces human errors in SQL
  • Helps non-technical users query databases

Why Use Gemini for Text-to-SQL?

Gemini AI is a powerful large language model (LLM) by Google that can generate accurate SQL queries from natural language. It is ideal for data engineers, software developers, and analysts who want to automate SQL generation.

Key Advantages:

  • Understands natural language
  • Generates syntactically correct SQL
  • Can work with multiple database systems like BigQuery, Postgres, and MySQL
  • Supports complex queries including joins, aggregations, and filters

Step 1: Define Scope and Database Access

Before building your Text-to-SQL tool, decide on:

  • Database type: BigQuery, Postgres, MySQL, etc.
  • Query type: Read-only SELECT queries first
  • Schema complexity: Single schema vs multiple schemas
  • User permissions: Ensure safe access and role-based control

Tip: Start small. Begin with read-only queries on a single schema to simplify testing.

Step 2: Represent Your Database Schema

LLMs need schema information to generate accurate queries. Provide a clear schema with:

  • Table names
  • Column names
  • Data types
  • Relationships (foreign keys)

Example Schema Representation:

Tables:
orders(
  order_id INT,
  customer_id INT,
  order_date DATE,
  total_amount FLOAT
)

customers(
  customer_id INT,
  name STRING,
  region STRING
)

Providing the schema prevents hallucinations and ensures SQL accuracy.

Step 3: Craft Effective Prompts for Gemini

Prompt design is critical for accuracy in Text-to-SQL. Your prompt should clearly instruct Gemini on how to generate SQL.

Example Prompt Template:

You are a SQL expert.
Generate a valid BigQuery SQL query.

Rules:
- Use only the tables and columns provided
- Do not invent tables or fields
- Return only the SQL query

Database schema:
{SCHEMA}

User question:
{USER_INPUT}

This ensures:

  • Correct SQL syntax
  • LLM focuses only on provided tables
  • Output is machine-parsable

Step 4: Call Gemini API

Once your prompt is ready:

  1. Inject the schema and user question
  2. Call the Gemini API
  3. Receive the SQL query as output

Example Pseudocode:

String prompt = buildPrompt(schema, userInput);
String sql = geminiClient.generate(prompt);

Always log prompts, input, and SQL output for debugging and auditing.

Step 5: Validate Generated SQL

Never execute LLM-generated SQL blindly. Implement a validation layer:

Checks to Include:

  • Only allow SELECT queries
  • Block INSERT, UPDATE, DELETE, DROP
  • Limit query execution rows (LIMIT 1000)
  • Validate table and column names
  • Parse SQL for syntax errors

Example Validation Rule:

if (!sql.trim().toUpperCase().startsWith("SELECT")) {
    throw new SecurityException("Only SELECT queries allowed");
}

Validation prevents data leaks, accidental deletion, and SQL injection risks.

Step 6: Execute SQL Safely

Use a read-only database user and enforce:

  • Query timeouts
  • Row limits
  • Cost controls (especially on BigQuery)

Return results to the user or optionally send results back to Gemini for plain-language explanations.

Step 7: Handle Errors Gracefully

Common issues include:

  • Invalid SQL
  • Missing columns
  • Ambiguous user queries
  • Schema mismatches

Best Practice: Retry with feedback:

The previous SQL failed:
"Column 'region' does not exist"
Fix the query using the schema.

This improves accuracy over time.

Step 8: Prevent Hallucinations

LLMs may hallucinate data if the schema is not included. Avoid this by:

✅ Providing full schema
✅ Using explicit prompt rules
✅ Validating SQL
✅ Restricting query scope

Production Enhancements

Once your tool works, consider:

Accuracy Enhancements:

  • Few-shot examples
  • Schema pruning
  • Query caching

Security Enhancements:

  • Column-level access control
  • PII masking
  • Query cost estimation

UX Enhancements:

  • SQL preview before execution
  • Confidence scoring
  • Editable SQL for power users

Example End-to-End Workflow

User: "List top 5 customers by total order value"
   ↓
Schema and prompt injected
   ↓
Gemini generates SQL
   ↓
SQL validation layer ensures safety
   ↓
SQL executed on database
   ↓
Results returned to user

Key Takeaways

  • Text-to-SQL allows natural language to SQL translation
  • Gemini AI can generate accurate, executable SQL
  • Always validate SQL before execution
  • Start small, read-only, and scale gradually
  • Secure your database credentials and enforce access control
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