Voice of the DBA

Tag: AI

  • ChatGpt Experiments–Getting Documentation

    I get asked questions by salespeople all the time. Usually this is because a customer is struggling and I try to educate the salesperson as well as give them something to send to the customer. Here’s a place I used ChatGPT to make this quicker, or at least try.

    This is part of a series of experiments with the ChatGPT and other AI systems.

    Asking a Question

    A customer asked about configuring SQL Backup Pro to use Amazon S3 for backup locations. Rather than search out the docs, find URLs, etc. I asked ChatGPT. Here’s what I got:

    Prompt: How do I configure SQL Backup Pro to send files to Amazon s3?

    Answer:

    To configure SQL Backup Pro to send backup files to Amazon S3, you need to set up an Amazon S3 storage account and configure SQL Backup Pro to use it as the destination for backup files. Here are the general steps to configure this integration:

    1. Create an Amazon S3 bucket: Log in to your Amazon Web Services (AWS) account and create an S3 bucket where you want to store the SQL Server backup files. Take note of the bucket name and the AWS access credentials.

    2. Install SQL Backup Pro: If you haven’t already, download and install SQL Backup Pro from Redgate Software. Ensure that it is properly installed and licensed.

    3. Open SQL Backup Pro: Launch SQL Backup Pro and open the SQL Server instance you want to configure for backups.

    4. Configure backup settings: In SQL Backup Pro, navigate to the Backup tab and configure the backup settings according to your requirements, such as database selection, compression level, and encryption settings.

    5. Configure backup destination: In the backup settings, choose the backup destination as “File (network or local drive)”.

    6. Set the backup path: Specify a local folder where SQL Backup Pro will temporarily store the backup files before transferring them to Amazon S3. This can be any local folder accessible by the SQL Server service account.

    7. Configure transfer settings: Under the “Backup files to the network or local folder” section, select the option “After the backup completes, copy files to another location or device”.

    8. Choose the transfer type: In the transfer settings, select the transfer type as “Amazon S3”.

    9. Enter Amazon S3 credentials: Provide the AWS access credentials (Access Key ID and Secret Access Key) associated with your Amazon S3 bucket. These credentials authenticate SQL Backup Pro to access and upload files to your S3 bucket.

    10. Specify the S3 bucket details: Enter the bucket name and the desired S3 region where the backup files should be uploaded.

    11. Set other transfer options: Configure additional transfer options, such as file naming, retention policies, and logging preferences.

    12. Save and test the configuration: Save the settings and perform a test backup to verify that SQL Backup Pro can successfully transfer the backup files to Amazon S3.

    Once the configuration is complete, SQL Backup Pro will automatically transfer the backup files to the specified Amazon S3 bucket after each backup operation.

    Note: The specific steps and options may vary slightly depending on the version of SQL Backup Pro you are using. It’s recommended to consult the product documentation or contact Redgate support for detailed instructions based on your specific version and environment.

    Did it Work?

    While this was generating, I actually looked for docs to evaluate whether this was good advice. I don’t completely trust the AIs yet. What I found was:

    The instructions from ChatGPT don’t reference specific URLs, which I think is something that I would hope for. If I were sending instructions to a client, they might figure things out from the ChatGPT answer, but they’d be annoyed.

    A good example of where domain knowledge is needed, and still some work. This might be helpful if I had links it the answer to quickly check things.

  • Using ChatGPT for Writing

    I have been experimenting more and more with ChatGPT on a regular basis. I’m interested and curious how this will help. I had a friend at one of the large FANG companies note that they found ChatGPT to be very helpful in their job, with assistance in various things. Their quote:

    ”…I used it to collaborate, having a design discussion, writing requirements, comments and document my code (!!), improve my writing and find the gap and provide suggestions…”

    I thought that was interesting, so I sent in this prompt: write a one page post in the style of http://www.voiceofthedba.com that summarizes this article https://www.madeiradata.com/post/home-experiments-and-stacked-bar-graphs

    I often find links that catch my eye, read them, and save them in a note for a future editorial. This time I asked ChatGPT.

    This is part of a series of experiments with the ChatGPT and other AI systems.

    The Results

    What I got from ChatGPT is below. I read it and then wrote my own editorial, which was called The Best Graph. Mine is quite different, and I think, it’s better. I’m not sure the response below summarizes the post correctly, I don’t know it’s in my style (maybe parts), and ultimately, it’s a summary, not a commentary. Maybe that’s on me. I didn’t specify an editorial or opinion.

    Read them both and let me know which one you like more, which you think works better.

    The ChatGPT Response

    In a recent blog post on Madeira Data Solutions, author Lisa B. shares her findings on the use of stacked bar graphs in visualizing data from home experiments. While stacked bar graphs are commonly used in business and marketing contexts, Lisa explores their potential usefulness in a personal setting.

    Lisa begins by describing her home experiments, which involve testing the efficacy of various cleaning products. She notes that while she initially used line graphs to track the results of her tests, she found that stacked bar graphs were more effective in illustrating the relative performance of each product.

    Lisa goes on to provide a detailed explanation of stacked bar graphs and their components, including the use of color-coding and labels to differentiate between different categories and subcategories. She also notes that stacked bar graphs are useful in highlighting changes over time and in comparing data across different groups.

    One of the key takeaways from Lisa’s post is the importance of choosing the right type of graph for a given dataset. While line graphs may be appropriate for tracking changes in a single variable over time, stacked bar graphs may be more useful when comparing multiple variables or subcategories. By selecting the appropriate type of graph, researchers can more effectively communicate their findings and insights to others.

    Overall, Lisa’s post provides a useful overview of stacked bar graphs and their potential applications in a personal, as well as professional, context. Whether you’re conducting home experiments or analyzing business data, it’s important to choose the right type of graph to effectively convey your findings.

  • AI Thoughts on the Build Keynote

    If you haven’t seen the Build 2023 keynote, it’s, well, interesting. At a surface level, it’s focused on AI and delivers some demos that many of us might find to be useful and intriguing. I didn’t attend the event (or watch it live), but I did see it a bit later and I made some notes, pausing the 30-minute talk a few times to think about what I’d seen.

    The opening lightly glosses over some of the AI enhancements to development tools and the environments that can be created quickly in GitHub or Azure. Some of us will like those, and maybe they’ll grow on me, but I tend to prefer a development environment on my own hardware, where I have unlimited compute power at a fixed cost. The first big announcement is then showing Copilot technology, essentially some ChatGPT-like abilities, embedded into Windows 11. The demo shows asking Windows where settings are, with the response including buttons to take actions, like setting dark mode. Minorly useful, though I think Windows search works fine. I can type “env” and get the “edit environment variables” in the results. I still have to click through to change things, but this doesn’t seem like a better use of AI, especially if I need to type “set dark mode” instead of “dark”.

    To be fair, the demo has the user asking for ways to adjust the system to get more work done. The suggestions are for dark mode and a focus timer. I knew about the former, but not the latter. Perhaps being able to ask for general assistance with tasks is useful as there are likely lots of features I know nothing about and wouldn’t even think to look for. There is also the option to drop a document, like a PDF, in the chat and Windows asks if the user wants the system to “explain”, “rewrite”, or “summarize” the document. The user clicks summarize and gets a summary of the document.

    There is also a demo with plugins that developers can write for Bing, such as one that uses a legal package to make a change to a document. While lawyers might be worried about their practices (or paralegals about job prospects), I’m more worried about a fundamental problem that many of us data professionals have seen in the past: garbage in, garbage out.

    In this case, if the AI model isn’t well-trained, can I really trust it to summarize a PDF or change a legal document? How can I tell if it’s wrong, or slightly off? In some sense, this reminds me of a high school report. It might summarize some text at an A level, or a D level. It’s up to me to judge that, and I can’t assume the results are good or bad.

    The important thing to keep in mind, however, is that we aren’t in that place with AI. We can’t just trust the AI. We are in a time when AI is an assistant, where it can help us complete a task or get something done a little quicker. We are still responsible. We still have to verify and do some work, but if the Copilot can automatically launch Jira and navigate to a ticket, or attach a document and create a short message to our team, that saves us time. It saves us tedium. It can make our jobs easier. We are still needed, but we don’t do all the heavy lifting.

    I do worry about some of the opportunities for plugins that developers will write strictly to monetize their efforts. If I want a shopping list, I don’t want it to go to Instacart. I want a list I can use. I realize that doesn’t necessarily make Microsoft or a developer any money, but not all the tasks and advances are about profit. Or at least, I hope they all aren’t. I hope some are here to just make the world better. For a quick view of what that could be, watch the keynote closing video.

    Steve Jones

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  • AI Experiments–Who am I and how am I remembered?

    I saw someone noting that AIs shouldn’t write obituaries. Since I maintain sqlmemorial.org, I wanted to see what would happen for me.

    This is part of a series of experiments with the ChatGPT and other AI systems.

    First, a simple prompt. This was delayed for about 20s while the AI was processing data. I assume it was looking over the sites for my name.

    2023-05-09 09_42_21-

    When it started to write, I thought it did a decent job.

    2023-05-09 09_46_34-Steve Jones SQL summary.

    I then tried for an obituary.

    2023-05-09 09_43_21-Steve Jones SQL summary.

    I very much appreciate the first part of the response. It isn’t good to do this publicly, though for figures, there are people who have to write and update these things so they are prepared. Not that they won’t edit this if someone dies, but often journalists are prepared.

    However, I also appreciate the kind words in the second para. I am not quite sure where/how this data was assembled, but it isn’t a part of any bio I’ve written.

    Not bad, ChatGPT.