I'm excited to be presenting at the 2025 SQA Annual Meeting next week - "Is it the Right Tool for the Job? How QA and Regulatory Professionals can Guide Software Decisions in Regulated Environments" As our familiar software tools become more feature-laden and generalized, it's critical to ensure that software meets clear use cases and basic user requirements. And with generative AI being shoehorned into every platform, defining if and how software is appropriate for the intended use has never been more important. – Brendan p.s. Enjoy this message? Read more at the Hyland Quality Systems website. |
I'm Brendan Hyland. I help regulated facilities transform their software, spreadsheets, workflows and documents from time-consuming, deviation-invoking, regulatory burdens, to the competitive advantage they were meant to be. Join me every week as we take a few minutes to explore, design, test and improve the critical systems we use in our facilities.
It’s the first step of the problem solving framework that I was taught back in Engineering school. Not ‘Plan’. Not “Define”. “I want to and I can”. That particular framework - the McMaster Six Step - never gained the popularity of the ones now used today, but in the end they all contain the same basic elements - research, planning & design, implementation, evaluation and iteration - just stated in different ways. However I’ve never really seen this particular element called out explicitly...
My eight year old son figured out a hack to make the music service work better for him. The kids have a Google smart speaker that is attached to a Spotify account so they can just ask for any of their favourite music. Anyone who has pre-teens in the house probably knows how much such a setup is used - all day every day. Coming from someone who had to run to the double-cassette boom box to press the record button any time a new favourite song came on the radio just so I could listen to it...
I’ve seen several quality leaders complain this week about their disappointment with generative AI - they’re not getting the results they expected. And I understand why - context is king! If you just ask AI to write a procedure or generate a quality document, you’ll get generic, mediocre output. Without enough context, AI can only produce something generic based on its training data. But how do you give it that context? By the time you’ve gone back and forth trying to “engineer the prompt” to...