I started writing in my previous blog post  about the importance of metrics and how they provide detailed insight into the development life cycle and identify key areas for improvement in software delivery for teams. Before delving into the different metrics themselves, though, it’s important to look at how we actually gather the data in the first place. After all, there is no way we can gain insight into our delivery if we don’t have access to the data that provides it. How

The software world may be looking to always deliver innovation and new features faster. However, delivery speed alone is no longer a meaningful indicator of success. Teams are expected to deliver quickly and  safely, sustainably, and predictably. Doing so though is a big challenge for many teams and the key to this improvement is understanding your software well and knowing what is going in in your software delivery process very well. This is where metrics play a critical rol

As the industry continues to rapidly adopt Large Language Models (LLMs) to power chatbots, copilots, search, automation, and decision support, one discipline has emerged as a critical success factor: prompt engineering. While LLMs are incredibly powerful, their outputs are highly sensitive to how they are instructed. The difference between a vague, unreliable response and a precise, trustworthy one often comes down to the quality of the prompt. Soemtihng which is a completely

In my previous post , we explored the big changes required in architecture to make LLMs successful. As those who are familiar with me will know that quality and testing are vital aspects of software architecture to me. So, I feel like I wouldn’t be able to explore the topic of software architecture without aspects of testing and quality. Testing and QA Must Be Built Into Architecture In classical software engineering – at least at a fundamental code level - testing often tend