Best practices when using ChatGPT with SQL Server

Integrating ChatGPT with SQL Server can significantly enhance your database management by automating SQL query generation, assisting in code reviews, and transforming data. However, using ChatGPT with SQL effectively requires understanding best practices to ensure accuracy, security, and optimal performance. This guide covers critical use cases and tips for a mindful implementation of ChatGPT in SQL Server.

Use Cases for ChatGPT and Microsoft SQL Server

• Query Suggestions: Automating SQL query generation. ChatGPT can help you quickly write complex queries based on your specific requirements, saving time and reducing the chance of errors, especially when working with large or unfamiliar databases.

• Code Review: Identifying performance and security risks. ChatGPT can review existing SQL queries and suggest optimisations, such as indexing, reducing nested subqueries, or spotting potential SQL injection vulnerabilities, helping you maintain efficient and secure code.

• Data Cleaning: Formatting and transforming data. ChatGPT can assist in creating SQL scripts to clean up data, such as removing duplicates, formatting dates, or standardising text entries, streamlining the data preparation process for analysis. 

• Report Generation: Writing queries for custom reports. By providing ChatGPT with the table schema and report requirements, you can quickly generate queries to extract the necessary data for dashboards, summaries, or detailed reports. 

• Learning and Troubleshooting: Explaining SQL concepts or debugging errors. ChatGPT can help clarify SQL functions, syntax, and error messages, making it a valuable resource for both beginners learning SQL and experienced users troubleshooting complex issues.

Best Practices for using ChatGPT with SQL Server

• Verify Output: Always double-check AI-generated queries before running them. While ChatGPT can be highly accurate, reviewing the queries for correctness is important, especially when dealing with complex logic or large datasets.

• Limit Sensitive Data: When sharing table schemas or prompts with ChatGPT, be mindful of data privacy. To maintain data security, avoid including sensitive personal data, passwords, or confidential business details.

• Performance Monitoring: Use ChatGPT for non-critical operations or as a starting point for query generation. After obtaining the query, test its performance on a smaller subset of data before applying it to production environments to ensure it runs efficiently. For more details, refer to our ChatGPT SQL query optimisation guide.

How to generate SQL Server queries with ChatGPT

You need ChatGPT to help answer data questions for your SQL Server database. To do this, you must provide ChatGPT with the schema of your table so it understands your data's structure. Let’s break down how to do this, using **example data** throughout the process.

1. Open SQL Server Management Studio (SSMS): Launch SSMS and connect to your SQL Server instance. Find the database containing the tables you want ChatGPT to work with. For this example, we’ll use a single table named Employees.

2. Extract the Table Schema:  In the Object Explorer, right-click the Employees table and select "Script Table as" > "CREATE To" > "New Query Editor Window." Example Data: This will generate the following table's structure:

    CREATE TABLE Employees (

         EmployeeID INT PRIMARY KEY,

         FirstName NVARCHAR(50),

         LastName NVARCHAR(50),

         Department NVARCHAR(100),

         HireDate DATE,

         Salary DECIMAL(18, 2)

     );

Note: This example schema lists columns and data types, which are crucial for ChatGPT to provide correct query responses.

3. Copy the Schema: Highlight the script text you see in the editor and copy it to your clipboard. The example schema you've copied contains essential details about how your table is set up.

4. Prepare Your ChatGPT Prompt:  Open ChatGPT and paste the example schema you copied. Now, add a specific data-related question. For example:

    I am using SQL Server 2019. Here is my table schema:

     CREATE TABLE Employees (

         EmployeeID INT PRIMARY KEY,

         FirstName NVARCHAR(50),

         LastName NVARCHAR(50),

         Department NVARCHAR(100),

         HireDate DATE,

         Salary DECIMAL(18, 2)

     );

Can you write a SQL query to find all employees in the 'Marketing' 

Example Data: The prompt above includes the schema details and specifies a clear question to guide ChatGPT in generating the correct SQL query.

5. Submit and Review: Send this prompt to ChatGPT. It will use your example schema to generate a SQL query tailored to the table's structure.

Why does this produce the best results?

This approach produces the best results because it gives ChatGPT the full context of your database. Sharing the table schema tells ChatGPT exactly what columns exist, their data types, and how they're structured. With this information, ChatGPT can generate precise queries that match your data, reducing the chances of errors or misinterpretations.

Additionally, as a bonus, including the SQL Server version (such as SQL Server 2012, 2016, 2017, 2019, or 2022) also helps fine-tune the query. Different versions have unique features and syntax, so specifying the version ensures ChatGPT uses the correct commands and functions for your setup. Together, these details allow ChatGPT to create the most accurate queries possible.