Voice of the DBA

Tag: AI

Don’t Create Workslop

I remember a time before email. Some of my first jobs were mostly based on paper being moved from person to person. I’m sure some of you remember these envelopes being used to communicate between individuals in an organization. I used those to send and get memorandums from others before we implemented email. Fortunately, our email implementation (cc:Mail) came soon after I started working in corporations.

Initially, people treated email much like paper mail inside organizations. However, over time, people started to treat email differently. It was easy to send an email around other work, so people started to send more messages than they ever would have with paper. They started to dash off notes quickly, sometimes too quickly, as an email might be followed by another email that includes a “I forgot this”. As instant messaging grew, we saw similar patterns where people were quick to send messages, regardless of whether they were important, well-thought-out, or even necessary.

As AI becomes more widely used in the workplace, there’s a similar tendency. People are quick to use AI to generate something and send it to others, often without due diligence on their part to ensure the work is at the quality level the other person expects. Some workers don’t double-check what they received from the GenAI tool, and it may not be complete enough to actually satisfy the requirements they were given. Maybe even worse, the result might not be targeted at the problem that was supposed to be solved.

I ran across an article on workslop, which is defined as AI-generated work that masquerades as good work. Instead of actually being what the organization needs, it’s sloppy, it’s low quality, or it misses the mark.

To be fair, I don’t think this is an AI issue. I have worked with plenty of people who produced low-quality output that wasn’t good enough for me to use. I’ve seen plenty of people not really try to produce quality results and do a poor job of completing the tasks they were assigned. With AI, they can do it quicker, which can be a problem, especially if they are producing things other employees depend on or need. The result might be some people be pushing their work onto others who have to spend time fixing (or completing) the copy/pasted GenAI results, taking away from the time others might spend on more important tasks.

In the technical world, we saw that in the 90s with VB6, where lots of technical and nontechnical people produced code quickly for an application that worked initially, but didn’t perform well, couldn’t be scaled to others, and wasn’t stable enough to run every day. Sometimes not stable enough for an hour. I suspect we’ll see a lot of AI-generated code that repeats this pattern. Not because the AI can’t generate good code, but the people using it won’t know how to ask for good code, with instructions about the types of code that create robust applications. They also won’t know (or won’t bother) to check the code for quality.

My guess is that the GenAI adaptation to lots of work will result in a lot of things produced, but at a lower quality than we might want. We’ll also see this phenomenon create inefficiencies as other workers have to return or repeat work. Fortunately, there is a lot of room for inefficiency in many organizations, so they can likely continue to function.

Those that learn to use GenAI well to produce higher quality work will do so faster and stand out from their peers. Of course, a big part of standing out is also developing strong soft skills and advocating for your accomplishments. Without that, you might find those who produce workslop, but talk about it well to others will stand out from you.

Steve Jones

Listen to the podcast at Libsyn, Spotify, or iTunes.

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November 17, 2025
SQL Server 2025 RegEx and AI
One of the language changes in SQL Server 2025 that I’ve seen a lot of people mention is the addition of RegEx functions to T-SQL. I decided to take a few minutes and try to examine how this feature works, and how I might use it. And more importantly, can AI help?

This is part of a series of experiments with AI systems.

Data with a Bit of a Pattern

One of the common things people use Regex for is validating email addresses.

I created a basic table in a database that looks like this:
```
CREATE TABLE customer

(
     customerid INT NOT NULL
         CONSTRAINT CustomerPK PRIMARY KEY,
     customeremail VARCHAR(200),
     validated TINYINT

)

GO
```
I then asked PromptAI to get me some test data like this:

I wasn’t connected to a database, but I still got code generated with insert statements. The AI also noted I had a “validated” column, and it populated that appropriately: 1 for good email, 0 for bad ones.

Once I run the inserts, I have data.

Now, can I check that the AI did this correctly?

REGEXP_LIKE

In SQL Server 2025, there are a number of regular expression functions, and REGEXP_LIKE is one of these. This function is designed to return a boolean if the string_expression matches the pattern_expression, where the latter is the regular expression.

Prompt sees this as a valid function for SQL Server 2025, which is great. I want to use the customeremail from the table as my string to check. For the regex, I need to create a regular expression, something I am not good at doing. I learned to do this at a very rudimentary level when writing Perl, but I’ve lost whatever little skill I used to have.

I saw an expression on StackOverflow for validating email. Let’s check if an AI can help.

In a few seconds, even on airport wi-fi (SFO as I write this), I get a response.

This looks like the expression from the SO answer, though much shorter. If you read SO, you know that the format isn’t as set and stable as we’d like. In any case, let’s see what this shows.

This is interesting. I get an error.

If I look at the docs, this says it returns a true/false, which I’d assume would convert to a 1/0, but let’s try an explicit case. When I do this, it works.

I can also do this in the WHERE clause, where the true/false thing just works. Let me move the function to the WHERE clause. If I do that, I have this code:
```
SELECT customerid,
        customeremail,
        validated

FROM dbo.customer

WHERE REGEXP_LIKE(customeremail, '^[A-Za-z0-9._%-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$') 
```
When I run this, I see what I expect:

Not a bad enhancement to the T-SQL language.

Summary

There’s a lot written on RegEx, so this post isn’t intended to delve deeply into what RegEx expressions you choose. Rather, I wanted to show the basics of this REGEXP_LIKE() function, and showcase one of the weird things I found when including this in the column list. I also haven’t looked at performance yet, though that certainly is something to be concerned about with larger datasets

There are other changes in SQL Server 2025, and I’ll try to examine some in the coming weeks.
November 12, 2025
Finding and Killing Blockers with Redgate AI Tech

Redgate has a research arm, called the Foundry, that has been experimenting with AIs and DBA tasks. This post shows how GenAI tech can be helpful to DBAs in finding blocking and removing the offending client.

This is part of a series of experiments with AI systems.

Redgate Runbooks

One of the experiments the Foundry is running is with something we’ve called Runbooks. Here’s the main screen, where I have a welcome and a chat window. This is like what I see in Claude.ai.

I have connected this to two instances in the settings, and given the tool permissions to run queries, but not execute commands. The first server is the Local 2022 Default and the second is the 2910-41433.

A Blocking Problem

I’m going to set up a blocking session with this code. Notice it opens a transaction and then performs an update.

In a second session, I’ll run this code. Notice this select is blocked and I have no results. The bottom shows this as “executing”.

Now I’ll go to the Runbooks and enter a question. In this case, I ask it what is wrong with my 2022 server, as if someone called me and said there was an issue. Imagine the “Select” query owner wondering why things aren’t returning right away.

The Redgate Runbook responds by saying it needs to run something. The first time, it asks me to approve this, which I did. Then it runs it and shows executed.

Below this I get some results of what’s returned. This isn’t different than SP_who2, but if you’ve used that tool, you often get a lot of system stuff. I Could use sp_whoisactive, but again, more results than I want without knowing anything. Here the Runbook as limited results to what I care about.

What’s more, the Runbook then tells me something about what it analyzes. This isn’t perfect, but it’s been better than what a lot of help desk/first line support people have told me.

If I know who 56 is, certainly I can ask them to close their tran. This isn’t perfect, but I can ask the Runbook to do this, as I do at the bottom of the image above.

It again asks me to run something, and when it does, I see the executed note.

If I go back to SSMS, I see the query completed.

I typically might not kill the session without more research. I could have asked for what this is, which I’ll do now for the 57 (blocked) session. What was running here? (since 56 was killed)

Again, I approve this and get results.

To me, that’s pretty cool. Using AI to help me get things done as a lever, rather than a replacement is useful. I could have set the AI checking while I finished another task, or used Slack/Teams to check with others as a troubleshoot. I could certainly let the AI run, but I want approvals. I could copy/paste the code to a tool to run it, but the AI let’s this run separately, while I could be multi-tasking with a phone call to the user, or to the help desk, or anything else. More importantly, if I had this AI working from a mobile phone (jump box, etc.) I could be doing minimal typing and have the tech working for me.

This isn’t a product, and unlikely to be one in its own right, but this is the type of thinking we do at Redgate. Harness AI, in a safe way, that’s useful.

And ingeniously simple.

Tom Hodgson runs the group that worked on this, and he have me a fun, unforgettable interview. You can watch here: https://www.red-gate.com/simple-talk/podcasts/coffee-chat-with-tom-hodgson/

Video Walkthrough

Watch this live

November 5, 2025
Data > Hype

There is a ton of hype now about using GenAI for various tasks, especially for technical workers. There are lots of executives who would like to use AI to reduce their cost of labor, whether that’s getting more out of their existing staff or perhaps even reducing staff. Salesforce famously noted they weren’t hiring software engineers in 2025. I’m not sure they let engineers go, but it seems they did let support people go.

For many technical people, we know the hype of a GenAI agent writing code is just that: hype. The agents can’t do the same job that humans do, at least not for some humans. We still need humans to prompt the AIs, make decisions, and maybe most importantly, stop the agents when they’re off track. I’m not sure anyone other than a trained software engineer can do that well.

I was listening to a podcast recently on software developers using AI, and there was an interesting comment. “Data beats hype every time, ” which is something I hope most data professionals understand. We should experiment with our hypothesis, measure outcomes, and then decide if we continue on with our direction, or if we need to rethink our hypothesis.

Isn’t that how you query tune? You have an idea of what might reduce query time, you make a change, and check the results. Hopefully you don’t just rewrite certain queries using a pattern because this has helped improve performance in the past without testing your choice. Maybe you default to adding a new index (or a new key column/include column) to make a query perform better? I hope you don’t do those last two.

AI technology can be helpful, but there needs to be some thought put into how to roll it out, how to set up and measure experiments, and get feedback on whether it actually produces better code and helps engineers. Or if it’s just hype that isn’t helping.

Ultimately, I think that this is especially true for data professionals, as the training of models on SQL code isn’t as simple or easy as it might be for Python, Java, C#, etc. For example, I find some models are biased more towards one platform (MySQL) than another (SQL Server). Your experiments should include using a few different models and finding out which ones work well and (more importantly) which ones don’t. We also need to learn where models actually produce better-performing code for our platforms.

If you’re skeptical of AI, then conduct some experiments. Try to learn to use the tool to help you, rather than replace you. Look for ways to speed up your development, or have an assistant handle tedious tasks. I have found that when I do that, I get benefits from AI that save a bit of typing.

From the Pragmatic Engineer podcast, the best way to deal with some of the hype on AI is with data, take a structured approach to rolling it out, throw in a lot of AB testing measures with different groups or cohorts, evaluate, and see what works well. One of the things the guest noted was that the most highly regulated and structured groups are having the most success with AI. Because they’re careful about rollout, and they are measuring everything. They’ve been measuring time spent, accuracy of tasks and more. Then they decide where and when to use AI, which might be the best advice you get.

Steve Jones

Listen to the podcast at Libsyn, Spotify, or iTunes.

Note, podcasts are only available for a limited time online.

November 5, 2025