Developing complex SQL queries can be a time-consuming and challenging task for data professionals. In this case study, we illustrate how using an AI-powered chatbot like ChatGPT can significantly reduce the time and effort required to build a complex SQL query. We will demonstrate the collaborative process between us and ChatGPT in solving a specific data extraction problem, emphasizing the back-and-forth nature of the interaction to ensure the final solution meets our requirements.


We needed to extract and process sales data from a JSON input, mapping actual and estimated sales values to their respective regions and years. The task involved working with multiple tables, complex conditional statements, and iterations. The objective was to develop a highly efficient and accurate SQL query to achieve this goal.

What we did

Collaborative Process:

We developed a custom application with the following features:

  • 1
    Understanding the problem

    We provided a brief explanation of the problem, and ChatGPT asked follow-up questions to gain a better understanding of the requirements.

    Example: ChatGPT asked us to clarify the structure of the JSON data and how to map the data fields to the appropriate sales year, region, and actual or estimate status.

  • 2
    JSON data exploration

    ChatGPT assisted us in writing queries to explore the JSON data structure, focusing on extracting relevant information such as sales year, region, actual or estimate status, and sales values.

    Example: ChatGPT suggested using OPENJSON to parse the JSON data and extract key-value pairs from the dataFields.data[0] and salesData.data[0] properties.

  • 3
    Iterative query development

    We collaboratively developed a complex SQL query with ChatGPT through an iterative process. ChatGPT provided suggestions and improvements, while we reviewed and adjusted the code. This involved a back-and-forth effort where ChatGPT sometimes provided solutions that did not entirely meet our needs, requiring further clarification and adjustment.

    Example: ChatGPT recommended using a WHILE loop to iterate through the JSON data and map the sales year, region, and actual or estimate status to their respective sales values. We reviewed the solution and provided additional information to refine it further.

  • 4
    Stored procedure review

    We shared a stored procedure containing the initial query, and ChatGPT identified potential issues and areas for improvement. ChatGPT suggested using table variables instead of temporary tables, reducing the number of loops, and simplifying the code.

    Example: ChatGPT advised us to replace the nested IF statements with a more streamlined CASE statement to determine the region and MedtrackRegionID based on the input field values.

  • 5
    Finalizing the solution

    ChatGPT helped us develop an optimized version of the stored procedure, incorporating all the improvements and suggestions. The final solution was efficient, accurate, and tailored to our requirements.

    Example: ChatGPT guided us in reorganizing the stored procedure, using table variables, and simplifying the code, resulting in a more efficient and maintainable solution.


This case study demonstrates the power of AI-assisted query development in reducing the time and effort required for complex SQL tasks. The collaborative process between us and ChatGPT, involving a back-and-forth effort, enabled the rapid development of an optimized solution that met our specific needs. This interaction highlights the importance of users reviewing AI-generated solutions and providing feedback to ensure accuracy and relevance. AI-powered chatbots like ChatGPT have the potential to revolutionize the way data professionals work, allowing them to focus on higher-level tasks while leveraging AI assistance for faster, more accurate query development.