AI Assisted Greenfield Software Development, Part 3: Generating the Process Instruction Files

by John Miller | December 30, 2025

In the third post of his series, John Miller explains how he creates instruction files that guide AI code generation. The key is being specific with prompts and tracking code sources to avoid messy output. John covers three instruction files: one for AI code quality standards, one mapping the human-AI workflow with review stages, and one defining a Git workflow for fast updates. He uses prompts to automatically generate and maintain these instructions, making the system flexible. The goal is making AI a reliable team member that speeds up development without breaking things or creating compliance issues.

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What Happened to Power BI?

by Ginger Grant | December 29, 2025

Ginger Grant explains that Power BI remains a healthy, actively developed product despite recent licensing changes and Microsoft's emphasis on Fabric; you can still use Power BI standalone, but Fabric provides compelling benefits for organizations that need scale, real‑time performance, and more modular development. By moving data engineering, security, and storage into Fabric’s OneLake and using features like Direct Lake, teams gain near‑instant, scalable reporting, better pipeline and notebook workflows, improved monitoring and validation, and easier collaboration than traditional Power Query approaches. In short, Fabric is not required, but it materially enhances Power BI for enterprise scenarios.

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What's the Big Deal?

by John Miller | December 1, 2025

John writes that AI-assisted coding continues programming's evolution from machine code to high-level languages, rather than revolutionizing it. The core challenge of bridging human intent and machine action remains constant. AI enables natural-language development and better collaboration, but developers are still essential for architecture, safety, and ethics. AI is a tool, not a replacement.

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Microsoft Fabric: The New Method for Organizing Data in the Azure Cloud

Ginger Grant | 11/1/2025

Ginger Grant presents Microsoft Fabric as a unified, modern solution for organizing and centralizing organizational data in the Azure cloud (and beyond) with dramatically simplified setup and governance. She explains how Fabric replaces the traditional mix of disparate tools (Data Factory, Databricks, Data Lake, IoT/Event hubs, and databases) with a single, configurable resource that orchestrates data from multiple sources—including multi-cloud and Snowflake—through seamless referencing or mirroring rather than heavy data import. Fabric includes broad tool compatibility (PySpark, T-SQL, Power Query, notebooks) and a predictable fixed-hourly pricing model, plus a centralized storage layer called OneLake. Overall, Fabric addresses complexity, cost, and collaboration challenges by delivering an integrated, cross-provider data platform.

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Self-Service Reporting: Setting Up Successful Reporting for Everyone

Ginger Grant | 10/7/2025

Ginger makes the case for giving business users the tools to create their own reports. When IT controls all data access, teams often resort to inefficient workarounds that create security risks and waste time. She shows how Microsoft Fabric and Power BI can solve this problem by giving users secure, well-governed access to the data they need. With the right setup, people can build their own reports without sacrificing data quality or creating chaos. The result? Less waiting on IT, more consistent data across the organization, and faster, smarter decisions.

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AI-Assisted Greenfield Software Development, Part 2: Core Instructions

John Miller

In this post, John Miller outlines a practical framework for AI‑assisted greenfield development that codifies how prompts, instruction files, and chat‑mode configurations work together to produce auditable, reusable code and documentation; he provides concrete templates (for AI provenance metadata, instruction/prompt/chatmode files, and a context‑validation prompt), a canonical chat workflow, and CI enforcement checks to ensure traceability, consistency, and quality of AI‑generated artifacts. John's guidance focuses on embedding standardized metadata, logging conversations and summaries, automating scaffolding, and using token‑efficient instruction patterns so teams can safely scale AI assistance while preserving compliance, reviewability, and continuous improvement across projects.

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AI-Assisted Greenfield Software Development, Part 1: Business Requirements

John Miller | 9/21/2025

John Miller introduces a series exploring incremental solution development with AI assistance, specifically GitHub Copilot. This post focuses on establishing business requirements for an academic system by leveraging Object Role Modeling (ORM), a methodology that ensures conceptual clarity, flexibility, and effective collaboration between technical and non-technical stakeholders. Miller highlights ORM’s ability to articulate business rules, detect modeling errors, and enforce data quality, thereby providing robust context for AI-driven implementations. He presents the academic system’s conceptual model, structured workflows, and critical business constraints—such as exclusion and irreflexive ring constraints—to inform solution design while ensuring compliance with rules. John says later posts will detail architecture, tech stack, and iterative AI-guided development.

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Sharing Instructions with the Team

John Miller | 8/30/2025

John addresses the practical challenge of streamlining shared coding standards and best practices using GitHub Copilot across multiple development projects. He explores how developers can avoid duplicating instruction files in each project repository by leveraging symlinks on Linux/macOS and NTFS junction points on Windows. By centralizing shared instruction files in a dedicated GitHub Copilot repository and creating scripts to automate the linking process, John provides a robust cross-platform solution that ensures consistency while minimizing maintenance overhead. The post includes technical details about symlinks and junctions, along with step-by-step scripts for creating these links, ensuring version control hygiene, and customizing for different operating systems. This approach not only simplifies collaboration but also helps teams maintain a single source of truth for shared instructions, making it an invaluable resource for managers, developers, and decision-makers in software projects.

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The Strategic Case of Open-Source Data Engineering

Ginger Grant | 8/29/2025

Ginger explores how companies can break free from the expensive 10-year cycle of proprietary data transformation tools by embracing open-source technologies like Python and Spark, which Microsoft Fabric now fully supports through its adoption of vendor-agnostic approaches and Medallion Architecture.

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When Database Alternatives Make Business Sense: A Microsoft Fabric Perspective

Ginger Grant | 7/31/2025

Ginger Grant examines the transition from traditional databases to Microsoft Fabric's analytics-focused architecture. Fabric uses Delta Parquet files in scalable lakehouses and warehouses, supporting familiar tools like T-SQL and Power BI while adding AI-powered natural language queries. While databases excel at fast transactions, Fabric offers cost-effective, petabyte-scale analytics with integrated AI tools, making it ideal for organizations prioritizing data analysis over transactional processing.

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Modes of Chatting with GitHub Copilot

John Miller | 7/30/2025

John Miller discusses the various chat modes available in GitHub Copilot, including Ask, Edit, Agent, and Custom modes, emphasizing their distinct functionalities and use cases. He highlights how these modes can streamline coding and automation tasks in developers' workflows by providing contextual guidance, code editing, autonomous task execution, and persona-based AI behavior. Miller also delves into custom chat modes, explaining how users can create tailored configurations to instruct Copilot on specific tasks, thereby promoting consistency and efficiency across teams. Additionally, the post addresses the cost implications of using different modes, particularly noting that while Ask and Edit modes are more cost-efficient with standard models, Agent mode incurs higher costs due to its premium quota demands.

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Turning Data into Information with Fabric

Ginger Grant | 7/2/2025

Microsoft Fabric addresses the challenge of data silos by creating OneLake, a unified data lake that consolidates organizational data from multiple applications like CRMs, HR systems, and accounting software. The platform transforms fragmented data into actionable information through shortcuts, real-time database mirroring, and integrated analytics tools. By combining lakehouse and warehouse structures with security controls, Fabric enables organizations to achieve a single version of truth for data-driven decision making across Power BI, Excel, and other Microsoft tools.

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Unlocking the Promise of Data-Driven Decisions: Why Microsoft Fabric Can Succeed Where Legacy Systems Failed

Ginger Grant | 5/30/2025

Ginger Grant examines the challenges organizations face in transforming data into data-driven decision-making. Despite the pervasive availability of data, many companies have struggled with disjointed systems and inaccurate data, leading to failed AI implementations and ineffective decision-making. Microsoft Fabric, supported by Azure's cloud capabilities, addresses issues inherent in past data solutions, including integration, storage, and visualization, to provide a cohesive approach for data-driven insights. Microsoft Fabric stands out with its open standards, low-code tools, and interoperability with platforms like AWS, Google Big Data, and Snowflake, making it accessible for diverse organizational needs. Ginger thinks Microsoft Fabric is a game-changer that can help companies connect, clean up and organize data, leading to intelligent business decisions through streamlined data processes. Future posts in this series will discuss the business considerations in how to connect the data using Microsoft Fabric to get to data-driven decision-making.

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Developing User Interfaces with GitHub Copilot, Part 4

John Miller | 5/29/2025

In this final installment of this series on AI-assisted user interface development, John M. Miller demonstrates how GitHub Copilot can efficiently implement client-side sorting in a data table using Vue.js. By providing specific prompts, Miller shows how Copilot can automate complex UI changes like adding sortable columns, which traditionally require multiple code and style updates for interactivity and user feedback. GitHub Copilot outlines a step-by-step approach to enhance table functionality, including adding state variables, computed properties, sorting logic, and UI indicators. To encourage better practices, John prompts GitHub Copilot to change its output into a reusable component of sortable table headers, encouraging modular design and reusability.

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Developing User Interfaces with GitHub Copilot, Part 3

John Miller | 4/30/2025

In "Developing User Interfaces with GitHub Copilot, Part 3," John Miller illustrates the process of integrating data visualizations into web pages using AI tools. His post focuses on leveraging GitHub Copilot to initially implement a CSS-based Sales Funnel visualization in a Vue.js component, which provides interactive features and visual appeal through hover effects and gradient backgrounds. John then has AI transition the visualization to a chart.js-based implementation, highlighting the library's capabilities in rendering responsive charts with enhanced interactivity and flexibility. John notes this effort didn't need manual coding.

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Developing User Interfaces with GitHub Copilot, Part 2

John Miller | 3/29/2025

In his second installment on using GitHub Copilot for user interface development, John delves into creating reusable components, specifically an `OwnerSelectDropdown`, to enhance consistency across multiple Vue views. By leveraging Copilot's new multi-file Copilot Edits feature, developers can apply AI-driven suggestions to streamline workflows for large-scale refactoring, such as encapsulating dropdown logic into components and ensuring these updates reflect across related files. John demonstrates the creation and integration of this component into various views, illustrating both the process and challenges involved. He addresses potential errors and emphasizes the iterative nature of refining AI-generated code with strategic prompts, highlighting Copilot's capability to handle syntax but underscoring the necessiry that a coder must review the generated code.

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Developing User Interfaces with GitHub Copilot, Part 1

John Miller | 2/24/2025

In his blog series, John Miller delves into the practical application of GitHub Copilot for developing and modifying user interfaces, using Vue.js as a demonstration framework. This initial post focuses specifically on integrating a new control—a dropdown component—into an existing Vue.js application used for lead management. John illustrates the process of modifying a component to enable the selection of individual lead owners and fetching corresponding data, demonstrating detailed steps on incorporating AI-driven suggestions into coding practices. John emphasizes the importance of providing GitHub Copilot with precise prompts to generate effective code and highlights the role of developers in directing AI through clear instructions and context.

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Adding Dependency Injection to an Existing Solution

John Miller | 1/30/2025

John Miller explores the challenges and benefits of refactoring legacy code by integrating dependency injection (DI) to improve software maintainability and testability. He addresses common issues in legacy systems, such as tight coupling and lack of interfaces, which hinder the ability to conduct effective unit testing. John emphasizes the importance of DI and interfaces in creating flexible, modular, and isolated code components, thereby facilitating easier testing and maintenance. Using a practical example, he illustrates how DI can decouple service classes from specific implementations and discusses the role of tools like GitHub Copilot in automating and accelerating the refactoring process. By implementing DI, developers can enhance code flexibility, allowing for seamless integration of alternative components and improving overall software quality.

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Test Generation Using GitHub Copilot

John Miller | 12/13/2024

John's blog post "Test Generation Using GitHub Copilot," explores the strategic advantages of employing AI, specifically GitHub Copilot, for generating test automation in software development. Acknowledging the risks of integrating AI-generated code in production environments, John suggests that test automation provides a safer entry point due to its non-production deployment. Highlighting test automation's role in exposing design flaws and enabling refactoring, the post demonstrates how Copilot can effectively generate unit tests, using a complex C# BankAccount class as a case study. John goes on to show examples of the process of generating tests using Copilot’s integration with various IDEs, emphasizing that despite occasional test adjustment requirements, AI-enhanced test automation facilitates higher code coverage, efficiency, and reliability. John advocates for embracing AI tools like Copilot to enhance the development workflow, paving the way for more robust and maintainable software systems.

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Unleashing the Power of GitHub Copilot in Visual Studio Code

John Miller | 11/11/2024

John Miller explores the transformative potential of GitHub Copilot, an AI-powered coding assistant created by GitHub and OpenAI. Leveraging machine learning on vast amounts of public code, Copilot offers context-aware code suggestions, enhancing productivity for developers by streamlining tasks such as writing functions or debugging. John provides a guide to integrating GitHub Copilot with Visual Studio Code, covering installation, authentication, and features like autocomplete, context-aware coding, multiline completions, and example-based learning. He delves into the functionality of the @workspace command, Copilot's chat commands, and various integration features within VS Code. The post emphasizes Copilot's ability to optimize the coding process, allowing developers to focus on creative problem-solving by reducing the burden of writing boilerplate code and improving overall workflow efficiency.

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Boosting Developer Productivity with AI Tools and Effective Strategies

John Miller | 10/14/2024

John Miller explores how AI is transforming the tech industry by enhancing developers' capabilities rather than threatening their jobs. He emphasizes that developers who embrace AI can automate repetitive tasks, enhance coding efficiency, and focus on more strategic work. Key insights from McKinsey and Microsoft highlight AI's role in accelerating code documentation, optimization, and writing, while also promoting deep work by minimizing interruptions. Improved developer experiences lead to higher product quality and employee satisfaction. John advocates for adopting an AI mindset to maintain a competitive edge and plans to delve deeper into AI's impact on the software development life cycle in future posts.

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AI Powered Database Queries

Markus Egger | 7/16/2024

In his blog post "AI Powered Database Queries," Markus Egger introduces a groundbreaking feature that allows AI-driven natural language prompts to generate and execute complex SQL database queries. This innovation differs from traditional AI Copilot features, which typically handle smaller datasets in a conversational manner. Instead, Egger's approach leverages large language models (LLMs) such as GPT-4 to interpret user prompts, generate accurate SQL queries, and handle vast amounts of data. The implementation includes multiple layers of security checks and verification processes to ensure the generated queries are safe and meet the intended criteria. Egger argues that while some database vendors are working on integrating similar features, the custom, scenario-specific AI data dictionaries and rigorous security protocols his team employs offer a more secure and tailored solution. This advancement stands to significantly enhance how organizations can interact with and extract value from their large datasets.

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Using AI to Generate Art for CODE Magazine

Markus Egger | 6/19/2024

In his blog post, Markus Egger outlines the innovative use of AI to generate art for CODE Magazine, addressing the challenge of creating cost-effective, high-quality artwork for numerous articles and social media platforms. Markus describes a streamlined process involving multiple AI systems: an AI that generates article synopses, another that crafts creative and contextually relevant prompts, and an image generation AI (e.g., OpenAI's Dall-E 3) that produces original visuals. Further refinement and formatting are handled by an AI-driven tool called "Olympus Image Composer." Markus emphasizes that this technology not only optimizes resources but also expands artistic opportunities, countering concerns about job elimination by highlighting an overall increase in art-related employment. This post provides valuable insights for managers, decision-makers, and developers interested in leveraging AI for content creation and operational efficiency.

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AI Briefing - Medicine

Markus Egger | 11/27/2023

This blog post highlights a recent presentation at a major U.S. medical institution, focusing on the integration of Artificial Intelligence (AI) in the medical industry. The post introduces the concept of "Executive Briefings on AI," tailored to the medical sector, encompassing hospitals and research applications. It delves into a scenario where AI assists doctors in emergency situations, even with limited prior knowledge of the patient's medical history. The presentation demonstrates a real-world example of AI in diagnosing medical emergencies, analyzing vital signs and medical history. A unique feature of this AI system is its multilingual capabilities, exemplified by diagnoses provided in different languages. The post emphasizes that such AI applications are not futuristic concepts but present-day realities. It aims to enlighten decision-makers in medical settings about the current potential and challenges of AI in healthcare.

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Copilot in Your Apps over Rectangular (SQL) Data

Markus Egger | 6/21/2023

In a recent State of .NET event, a remarkable demonstration showcased the capabilities of a custom Copilot utilizing Azure OpenAI. This large language model (LLM)-powered tool showcased its ability to navigate and reason over data across numerous SQL Server tables, challenging the perceived limitations of LLMs. By seamlessly integrating with a custom application, this Copilot unveiled the potential of intelligent machines and software working together to make informed decisions and optimize processes with unprecedented precision. This demo not only provided a glimpse into the future of technology but also emphasized the boundless possibilities of .NET and AI integration. The era of intelligent entities capable of learning, reasoning, and interacting with data has arrived, and the future promises even more exciting advancements.

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