ai

In this post, I’ll walk you through the major refactoring of Azure Quick Review (azqr), where I used GitHub Copilot’s plan mode and agent mode while supervising every change. My role was purely architectural: I defined what needed to change, reviewed every proposal, and guided the AI through the process. TL;DR # I refactored Azure Quick Review (azqr) without writing a single line of code by using GitHub Copilot’s plan mode (to design the architecture) and agent mode (to implement it). The refactor eliminated massive technical debt, 72 scanner packages, 72 command files, hundreds of ARM calls and replaced them with a centralized scanner registry, batched Azure Resource Graph queries, a modular pipeline, dynamic command generation, and unified throttling policy.

In this post, I’ll show you how to deploy Azure AI Foundry connected to Bing Grounding using Terraform. This setup enables you to leverage Azure’s powerful AI capabilities and enrich them with Bing’s search data, all managed as code for repeatability and automation. Prerequisites # Before you begin, make sure you have:

In this post, we will explore how to create custom evaluators to evaluate your Generative AI application locally with the Azure AI Evaluation SDK. The results of the evaluation can be uploaded to your Azure AI Foundry project where you can visualize and track the results. Prerequisites # Before you begin, ensure you have the following: