Cloud as a Story - Vunvulea Radu

 Today, most AI-generated platform engineering work still relies on classical prompt engineering: describe the outcome, add context and constraints, then iterate until the pipelines, infrastructure, and operational assets are “good enough.” More structured methods and tools (such as BMAD or SpecKit) are still relatively uncommon for tasks like DevOps pipelines, infrastructure-as-code, operational documentation, and production-readiness controls. That’s why I ran this comparison: I wanted to see whether structured approaches can materially outperform normal prompting on a realistic platform engineering task. I compared four approaches to generate pipelines, cloud infrastructure, and an operational layer for an existing application running on Azure App Service: SpecKit used with normal prompting, classical prompt engineering, SpecKit (method-driven), and BMAD. The goal wasn’t just “does it compile?”—it was whether the output looked like something a real platform team could run ...

 For a long time, the focus was clear: infrastructure, development, data, security and DevOps. AI was there, but more as an extra topic. Now this is changing. AI is becoming part of normal delivery, and as a result, the certifications that matter are also changing. This is not only about learning a new technology. It is about showing that our teams can design, build and deliver AI-native solutions in a real project environment. The most relevant AI certifications now are: AWS AWS Certified AI Practitioner AWS Certified Machine Learning Engineer – Associate AWS Certified Generative AI Developer – Professional Microsoft Azure Microsoft Certified: Agentic AI Business Solutions Architect Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure AI Fundamentals GitHub GitHub Copilot Certification AWS is making this shift in a practical way. An AI Practitioner helps create broad AI literacy. A Machine Learning Engineer – Associate brings more engineering depth. Generati...

 In my previous article, I looked at SpecKit without extensions and SpecKit with extensions, trying to understand how much structure really helps when we use AI to generate code. This post is a follow-up of that work. I kept the same prompt, tools, and evaluation method, but added a third approach: BMAD (BMad Agentic Development). From the beginning, BMAD felt different. SpecKit guides the AI through clear workflows. BMAD, on the other hand, feels like a small virtual team that thinks first, plans more, and then writes code. This difference shows clearly in the output. What impressed me most was simplicity. Even if BMAD did not win on all linting scores, the code was much easier to read and reason about. The Halstead cognitive metrics showed a big gap that classic linters do not really capture. In simple words, the BMAD code is easier for a human brain. Testing was another strong signal. BMAD produced the highest number of tests and almost 99% coverage, while also having the ...