3 years ago, we wrote our “Generative AI guidelines at SQLBI”.

We think it’s time to update them. Over the past 3 years, the world has changed: it is simply no longer possible to avoid using generative AI services for a multitude of purposes. Therefore, we want to update our guidelines to clarify how we use these tools.

First of all, our two simple concepts did not change:

  • We look forward to using AI to improve productivity: our productivity and the productivity of our readers.
  • Whenever we publish content generated by AI engines, we will always make that clear to our readers.

While these principles are still valid, I want to update the considerations I wrote three years ago.

I want to start with our current adoption in production. We produce video courses about DAX and Data Modeling for the international market. Thanks to AI, we improved the quality of subtitles in the original language (English) and their derived translations, which now benefit from higher quality. There is still a human review at the end of the loop, but we know that the quality of our video courses has definitely improved.

We can use AI for DAX analysis and rarely in DAX coding, and we perform a human review before publishing. While this is not the case for the articles and books we publish, we have seen improvements in many models for simple, recurring tasks that generate DAX code, and we have started using these tools for testing, evaluation, use case preparation, and demos. We see, almost daily, that knowing DAX allows users to write more structured prompts that prevent LLMs from going down the wrong path. For now, knowing DAX is still an advantage even if you do not write it directly.

We use AI in other stages of the development of content and semantic models. We do not create articles with AI. However, we use AI across different parts of the production process, primarily to improve the quality of the final result rather than just to increase our productivity. Providing tools to help AI agents be more accurate and efficient is an area we are exploring.

We will improve the consumption experience on our websites for users and AI agents. We did not integrate AI services into our websites as we intended to three years ago. It seems more productive to look at how to collaborate with AI agents. In a world where AI agents do not pay for training, this access might not remain free. However, we are far from understanding what constitutes a sustainable economic model for advanced content.

We will always be transparent when using AI-generated technical content. Compared to three years ago, I added “technical” to the previous sentence. We use AI to generate the comics in the SQLBI newsletter, and a full disclaimer seems excessive for a comic named “AI BI Blunders”. We want to keep our freedom to have fun, and the risk that someone takes things too seriously is a price we are willing to pay to put a smile on your face (and definitely ours).

Rereading my post, I’ve managed to keep it shorter than the one I wrote three years ago. So, let me add one short pro tip: use AI to improve your productivity, but don’t become a victim of AI. If you stop thinking, you become “disposable”. Your added value is in applying the 5 to 10% of corrections needed on the AI output. In the case of DAX, probably more than 5 to 10%, especially if you also need efficient code.

Thank you for your time reading it!