Content by hidde de smet (14)

Hidde de Smet explains practical frameworks and real-world techniques for effective prompt engineering and context engineering with LLMs and agent tools, including GitHub Copilot, helping AI practitioners move from vague queries to reliable, production-grade results.

Hidde de Smet explains how teams can move from individual AI-powered workflows to collaborative, spec-driven development. Explore practical team setups, CI/CD integrations, and advanced architecture strategies to grow your next Microsoft-focused project.

Hidde de Smet delivers a comprehensive field guide for developers mastering AI-assisted and spec-driven development. This post, Part 3 of his series, dives into debugging, best practices, troubleshooting, and automation for production-ready workflows.

Hidde de Smet continues his AI-assisted development series by demonstrating the full Spec-Kit workflow—detailing how to move from requirements to production-ready code using .NET 9, Blazor, and GitHub Copilot. A must-read for software engineers adopting modern, spec-driven workflows.

Hidde de Smet kicks off a deep-dive series on mastering AI-assisted development, highlighting why uncritical 'vibe coding' falls short and how specification-driven approaches like GitHub’s Spec-Kit help teams achieve robust production code.

Authored by Hidde de Smet, this guide provides a deep dive into the creation and operation of an AI Center of Excellence (CCoE), offering practical frameworks and strategies for coordinated, effective enterprise-wide artificial intelligence adoption.

Hidde de Smet offers an expert comparison of Azure Bicep, Terraform, and OpenTofu for Infrastructure as Code. This comprehensive post supports infrastructure and DevOps professionals in selecting tools for Azure, multi-cloud, and open-source strategies.

In this comprehensive guide, Hidde de Smet documents the step-by-step evolution of Terraform infrastructure for Azure. The post provides real-world insights and actionable patterns for teams modernizing their infrastructure-as-code, from basic setup to advanced automation and governance.

In this concluding article, Hidde de Smet guides readers through defining success metrics, piloting, and essential learnings for effective and responsible AI project implementation.

Hidde de Smet presents Part 2 of his series on validating AI projects. This installment demonstrates practical uses of an AI decision framework and examines essential ethical considerations—such as bias, transparency, and workforce impact—when evaluating AI initiatives.

Hidde de Smet shares Part 1 of a 3-part series on validating AI initiatives, focusing on a decision tree framework that helps organizations determine if AI is the best fit for solving their business problems.

Written by Hidde de Smet, this detailed guide walks readers through each stage of building and deploying an image classification solution using machine learning, covering both conceptual and practical considerations.

Written by Hidde de Smet, this article delves into the Model Context Protocol (MCP), highlighting its design, features, and transformative impact on AI integration for organizations such as Microsoft.

Written by Hidde de Smet, this article explores GitHub Copilot's Agent Mode, highlighting how it transforms the coding workflow by supporting natural conversations, interactive problem-solving, and step-by-step guidance directly within your development environment.