That’s awesome, thanks for sharing!

Do you use the English translations for tables and columns to provide it with a clearer ‘ontological model’ or can it manage just fine with the ‘raw’ ids?

We do not use any translations. We just use the table name (tab_id) and columns (col_id) and that’s giving us pretty concise and accurate descriptions. We’re planning on adding the data types and even the references to see if that results in an even better description

This is the prompt that’s used to generate the descriptions:

You are Datamodeller-GPT. A senior datamodeller who assists in creating brief  descriptions of tables you've been given in the following format: 

atable_name] = >column_name];=column_name] etc.  

Use a maximum of 2 sentences. Do NOT list the columns.

How about generating unit tests? That's quite a bit more involved but especially for simple cases it should be doable already with smart prompting

That's an interesting use-case! I've actually generated some unit tests for some SQL code as a test. So, prompting wise it should be doable for sure.

With the new browsing functionality for GPT4+ you could give it the Thinkwise documentation for unit testing and let it generate unit tests based on those guidelines for example. 

We're actually curious to hear more use-cases from within the community and maybe even demo's? Let's inspire each other 🤩

Interesting. I have been experimenting (and will continue to do so) with ChatGPT to create SQL code. I think that especially in the low-code world ChatGPT or alike could be helpful with generating code. Right now it is possible to have ChatGPT create simple peaces of code.

When asking: create T-SQL code to list the items from the database mail for SQL Server 2019

It replies with an almost correct answer:

USE msdb;

-- List all sent items
SELECT sent_item_id,
       recipients,
       subject,
       sent_status,
       sent_date
FROM sysmail_sentitems;

-- List all unsent items
SELECT unsent_item_id,
       recipients,
       subject,
       last_mod_date
FROM sysmail_unsentitems;

Correction is easy by replying: leave out the sent_item_id

For a more complex problem more input is required. In the example below I asked for:

Write a T-SQL procedure to export invoice-data from a table to an xml-file using UBL2.1 and Peppol. The invoice-data contains invoice_number, invoice_date and each invoice record has multiple child records containing the invoice lina - data.

The result is not what I need (see code below) but I think that in the near future ChatGPT or alike will be able to write procedures and functions that are quite complex. After this summer I will actually start a project to export invoices to UBL/Peppol standards, and I plan on using ChatGPT to do a part of the project as an experiment.

-- Create a stored procedure to export invoice data to XML
CREATE PROCEDURE ExportInvoiceDataToXML
AS
BEGIN
    -- Declare variables
    DECLARE @XML XML

    -- Generate XML using UBL2.1 and Peppol standards
    SET @XML = (
        SELECT
            invoice_number AS 'Invoice/ID',
            invoice_date AS 'Invoice/IssueDate',
            (
                SELECT
                    invoice_line_number AS 'InvoiceLine/ID',
                    invoice_line_data AS 'InvoiceLine/Description'
                FROM
                    YourInvoiceLineTable
                WHERE
                    YourInvoiceLineTable.invoice_number = YourInvoiceTable.invoice_number
                FOR XML PATH(''), TYPE
            )
        FROM
            YourInvoiceTable
        FOR XML PATH('Invoice'), ROOT('Invoices'), TYPE
    )

    -- Export XML to a file
    DECLARE @FileName NVARCHAR(255) = 'C:\Path\To\Your\File.xml'
    DECLARE @FileID INT
    EXEC sp_xml_preparedocument @FileID OUTPUT, @XML

    EXEC sp_xml_removedocument @FileID

    PRINT 'Invoice data exported to XML file: ' + @FileName
END

I have tried two things.
(I don't have my exact prompts and the results at hand)

Automating the design of an application.
I asked something like: Give me the SQL to create a simple CRM database.
Result was surprisingly ​good. Nice set of tables, columns, relationships, datatypes etc.
This indicates that in the future we can leave designing the basics of an application to AI and we can focus on what has to be different for a specific customer.

Automatic legacy code conversion.
I asked something like: Convert this Oracle PL/SQL code to Microsoft T-SQL.
Result was very good, I could not improve it much.
If we would have permanent access to a fast AI API, with an unlimited number of calls and unlimited length of input, I would build this into our Upcycler tomorrow.

I also tried a more difficult one, Uniface code to Microsoft T-SQL.
This did gave a result, which in itself is already impressive.
But even with more elaborate prompting it was not very good/usable. The conceptual differences between the platform make a one-on-one code conversion pointless.
Although I have handled a Uniface customer that had thousands of routines with just business rules, using it on those routines alone would already be great.