Getting Started with PromptQuery: Your First SQL Query

Welcome to PromptQuery! If you're new to AI-powered SQL generation, this guide will walk you through creating your first query using natural language. By the end of this tutorial, you'll understand how to leverage PromptQuery's context-aware AI to generate accurate SQL queries for your database.

What Makes PromptQuery Different?

Before we dive in, it's important to understand why PromptQuery works differently from other AI SQL tools. Unlike generic AI assistants that guess your database structure, PromptQuery connects directly to your database and uses your actual schema, tables, columns, and relationships to generate queries that work with your real data.

This means:

✅ Queries reference your actual table and column names
✅ JOINs use your real foreign key relationships
✅ Generated SQL respects your database constraints
✅ Queries leverage your indexes for better performance

Prerequisites

To follow along with this tutorial, you'll need:

A PromptQuery account (sign up free)
Access to a database (PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, or Oracle)
Basic understanding of SQL concepts (helpful but not required)

Step 1: Connect Your Database

The first step is connecting PromptQuery to your database. This allows the AI to understand your schema and generate accurate queries.

How to Connect

Log in to PromptQuery and navigate to the Connections page
Click "New Connection" and select your database type
Enter your connection details:
- Host/Server address
- Port number
- Database name
- Username and password
- (Optional) SSL settings for secure connections
Test the connection to ensure PromptQuery can access your database
Save the connection - it will be available for future queries

**Security Tip:** PromptQuery never stores your database passwords. Connections are encrypted and credentials are handled securely. You can also configure what metadata PromptQuery accesses from your database.

What Happens During Connection?

When you connect a database, PromptQuery:

Analyzes your database schema
Identifies tables, columns, and data types
Maps foreign key relationships
Discovers indexes and constraints
Builds a context map for AI query generation

This process happens automatically and typically takes just a few seconds.

Step 2: Open the SQL Editor

Once your database is connected, you're ready to start writing queries. Navigate to the SQL Editor in PromptQuery, where you'll see:

Connection selector - Choose which database to query
Query input area - Where you'll write natural language prompts or SQL
AI assistant panel - For generating and optimizing queries
Results area - Where query results will appear

Step 3: Write Your First Natural Language Query

Now for the fun part! Let's generate your first SQL query using natural language.

Example 1: Simple Data Retrieval

Start with something simple. In the query input area, type:

Show me all customers from the customers table

PromptQuery will:

Recognize you want to query the customers table
Generate the appropriate SELECT statement
Display the SQL it created
Execute the query and show results

Generated SQL:

SELECT * FROM customers;

Example 2: Filtered Query

Try a more specific request:

Find all orders placed in the last 30 days with a total amount greater than $100

PromptQuery understands:

You want to filter by date (last 30 days)
You want to filter by amount (> $100)
It uses your actual column names (order_date, total_amount, etc.)

Generated SQL (example):

SELECT * 
FROM orders 
WHERE order_date >= CURRENT_DATE - INTERVAL '30 days'
  AND total_amount > 100;

Example 3: Query with JOINs

Ask for data across multiple tables:

Show me customer names and their order totals from the last month

PromptQuery automatically:

Identifies the relationship between customers and orders tables
Creates the appropriate JOIN
Groups and aggregates data correctly

Generated SQL (example):

SELECT 
    c.customer_name,
    SUM(o.total_amount) AS total_orders
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '1 month'
GROUP BY c.customer_name;

Step 4: Review and Refine Generated SQL

After PromptQuery generates SQL, you'll see:

The generated query - Review it to ensure it matches your intent
Query explanation - Understand what the query does
Execution results - See the data returned
Optimization suggestions - Get tips for better performance

Reviewing Generated Queries

Always review the generated SQL before running it on production data. Check:

✅ Does it reference the correct tables?
✅ Are the column names accurate?
✅ Do the filters match your requirements?
✅ Are JOINs using the right relationships?

Making Adjustments

You can:

Edit the SQL directly if you need to make changes
Regenerate with a more specific prompt
Ask for optimization if the query seems slow
Request an explanation if something is unclear

Step 5: Execute and View Results

Once you're satisfied with the generated SQL:

Click "Run Query" or press Ctrl+Enter (Windows/Linux) or Cmd+Enter (Mac)
View results in the results panel
Export data if needed (CSV, JSON, Excel formats available)
Save the query to your query history for future reference

Tips and Tricks for Better Results

Now that you've written your first query, here are some strategies to get the most accurate results from PromptQuery:

1. Be Specific About Your Intent

Less effective:

Get customer data

More effective:

Show me customer names, emails, and registration dates for customers who signed up in 2024, ordered by registration date

Being specific helps the AI understand:

Which columns you need
What filters to apply
How to sort the results

2. Reference Your Actual Schema

When possible, mention your actual table and column names:

Good:

Find all users in the users table where status equals 'active' and created_at is after January 1st, 2024

This helps PromptQuery match your exact schema structure.

3. Specify Aggregations Clearly

For aggregate queries, be explicit about what you want:

Good:

Calculate the total revenue, average order value, and number of orders per month for the last 6 months

Less clear:

Show me sales stats

4. Use Business Language

You don't need to know SQL syntax. Use natural business language:

"Show me..." → SELECT
"Find all..." → SELECT with WHERE
"Calculate the total..." → SELECT with SUM()
"Group by..." → GROUP BY
"Only show..." → WHERE filter

5. Combine Multiple Requirements

You can ask for complex queries in a single prompt:

Show me the top 10 customers by total order value, including their names, email addresses, and the number of orders they've placed, but only include customers who have placed at least 5 orders

PromptQuery will generate the appropriate SQL with:

JOINs to combine customer and order data
Aggregations (SUM, COUNT)
Filtering (HAVING clause for the minimum order count)
Sorting (ORDER BY with LIMIT)

6. Ask for Explanations

If you're learning SQL, ask PromptQuery to explain queries:

Explain what this query does: [paste SQL]

You'll get a plain English explanation of:

What tables are involved
How data is filtered
What aggregations are performed
How results are sorted

7. Optimize Existing Queries

Have a slow query? Paste it and ask:

Optimize this query for better performance

PromptQuery will:

Suggest index usage
Recommend JOIN optimizations
Identify potential bottlenecks
Provide an improved version

8. Handle Edge Cases

Mention edge cases in your prompt:

Find all orders from last month, but exclude cancelled orders and handle NULL values in the shipping_date column

Common Patterns and Examples

Here are some common query patterns you can use:

Finding Recent Records

Show me the 20 most recent orders

Filtering by Date Range

Find all customers who registered between January 1st and March 31st, 2024

Aggregating Data

Calculate total sales, average order value, and number of orders for each product category

Finding Relationships

Show me all products that have never been ordered

Complex Filtering

Find customers who have placed more than 10 orders and spent over $1000, but haven't ordered in the last 30 days

Sorting and Limiting

Show me the top 5 best-selling products by quantity sold this year

Troubleshooting Common Issues

Issue: Generated query references wrong table names

Solution: Be more specific in your prompt. Mention the exact table name, or check that PromptQuery has access to your schema metadata.

Issue: Query returns unexpected results

Solution: Review the generated SQL and check:

Are filters applied correctly?
Are JOINs using the right relationships?
Are data types matching (dates, numbers, strings)?

Issue: Query is slow

Solution: Ask PromptQuery to optimize the query. It will suggest index usage and query improvements based on your database structure.

Issue: Missing columns in results

Solution: Explicitly mention which columns you need in your prompt:

Show me customer_id, name, email, and phone_number from the customers table

Next Steps

Congratulations! You've written your first SQL query with PromptQuery. Here's what to explore next:

Try more complex queries - Experiment with JOINs, aggregations, and subqueries
Explore query optimization - Learn how to improve query performance
Use query history - Save and reuse your favorite queries
Connect multiple databases - Work with different database types
Customize metadata access - Configure what schema information PromptQuery uses

Best Practices

As you continue using PromptQuery, keep these best practices in mind:

✅ Review generated SQL before executing on production data
✅ Start simple and add complexity gradually
✅ Use specific prompts for better accuracy
✅ Save useful queries to your query history
✅ Test queries on development/staging databases first
✅ Understand the generated SQL - use explanations to learn

Conclusion

You've learned the basics of using PromptQuery to generate SQL queries from natural language. The key is being specific about what you want and leveraging PromptQuery's understanding of your actual database structure.

Remember: PromptQuery works best when it knows your schema. Keep your database connections active, and the AI will generate increasingly accurate queries as it learns your database structure.

Ready to write more queries? Start using PromptQuery now and experience the power of context-aware SQL generation!

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