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title AI-Assisted Engineering
description Guide for using AI-powered tools like GitHub Copilot when working with the AI on Edge Flagship Accelerator
author Edge AI Team
ms.date 2025-07-18
ms.topic how-to
estimated_reading_time 5
keywords
ai-assisted engineering
github copilot
development workflows

AI-Assisted Engineering

This guide covers how to effectively use AI-powered tools, particularly GitHub Copilot, when working with the AI on Edge Flagship Accelerator.

Official Documentation

For comprehensive information about GitHub Copilot and VS Code integration, refer to the official documentation:

Project-Specific AI Resources

This repository includes specialized configurations and resources to enhance AI assistance:

Copilot Instructions

The repository includes comprehensive GitHub Copilot instructions in .github/copilot-instructions.md that provide:

  • Automatic Context Discovery: AI automatically finds and uses relevant project context
  • Convention Enforcement: Ensures all AI-generated code follows project standards
  • Component Understanding: Deep knowledge of the project's component and blueprint architecture
  • Markdown Standards: Automatic compliance with documentation formatting requirements

These instructions are automatically applied to every Copilot interaction, ensuring consistent, high-quality assistance.

Repository AI Guidance Files

The project contains specialized AI guidance files organized across different directories:

Core Guidance (/copilot/)

Comprehensive guidance files referenced by the main copilot instructions:

  • deploy.md - Deployment guidance and best practices
  • getting-started.md - Getting started guidance for new contributors
  • bicep/bicep.md - Bicep development guidance and standards
  • bicep/bicep-standards.md - Bicep coding standards and best practices
  • terraform/terraform.md - Terraform development guidance and standards
  • terraform/terraform-standards.md - Terraform coding standards and best practices

Note: Comprehensive guidance for Python scripting, Bash, and C# conventions are provided by the hve-core VS Code extension and loaded automatically when installed.

Context Instructions (/.github/instructions/)

Instruction files designed to be attached to Copilot context using Add Context > Instructions:

File Name Context/Language Description
bash.instructions.md Bash/Shell Scripting Comprehensive guidance for bash script development and shell command execution
bicep.instructions.md Azure Bicep Infrastructure as Code implementation guidance for Azure Bicep development
commit-message.instructions.md Git/Version Control Standardized commit message formatting using Conventional Commit patterns
csharp.instructions.md C#/.NET Development standards and practices for C# code implementation
learning-coach-schema.instructions.md Learning Instructions for AI coaches managing learner progress tracking in the Learning platform
python-script.instructions.md Python Python scripting standards and conventions for automation and tooling
shell.instructions.md Shell Environments General shell environment and command-line interface guidance
task-implementation.instructions.md Task Management Systematic process for implementing comprehensive task plans and tracking progress
terraform.instructions.md Terraform Infrastructure as Code implementation guidance for HashiCorp Terraform development

Reusable Prompts (/.github/prompts/)

Prompt files for specific tasks that can be invoked using /prompt-name in Copilot chat:

Prompt Name Invocation Description Use Case
csharp-tests.prompt.md /csharp-tests C# test development guidance Creating unit and integration tests
deploy.prompt.md /deploy Deployment workflows and best practices Infrastructure deployment assistance
getting-started.prompt.md /getting-started Project onboarding and initial setup guidance New contributor onboarding
iotops-version-upgrade.prompt.md /iotops-version-upgrade Azure IoT Operations version upgrade process Updating IoT Ops components to latest versions
python-script.prompt.md /python-script Python scripting standards and patterns Python automation and scripting
terraform-from-blueprint.prompt.md /terraform-from-blueprint Converting blueprints to Terraform Translating blueprint designs to infrastructure code

Note: Additional prompts for ADR creation and prompt engineering are available through the hve-core VS Code extension.

Enhanced Custom Agents (/.github/agents/)

Advanced agent files with comprehensive tool access for specialized coaching and workflow assistance:

  • adr-creation.agent.md - Interactive architectural decision record creation with comprehensive research and analysis capabilities
  • edge-ai-project-planner.agent.md - Edge AI project planning and solution architecture guidance
  • learning-kata-coach.agent.md - Interactive kata coaching with enhanced tool access
  • learning-lab-coach.agent.md - Complex training lab coaching for multi-component systems
  • security-plan-creator.agent.md - Security planning and assessment guidance for project implementations

Note: Task planning and prompt engineering agents are available through the hve-core VS Code extension.

Using Repository AI Resources

Applying Context Instructions

  1. Use Copilot Chat: Add Context > Instructions > Select the instruction file
  2. Add your specific context (files, folders, etc.)
  3. Provide your development prompt
  4. Instructions are automatically applied to ensure consistency with project standards

Invoking Reusable Prompts

  1. In VS Code, use Command Palette: Chat: Run Prompt and select desired prompt
  2. Or type /prompt-name directly in Copilot chat (e.g., /pull-request, /getting-started)
  3. Follow the guided workflow provided by the prompt

Using Enhanced Custom Agents

Custom agents provide specialized AI coaching with enhanced tool access, changing the system prompt in addition to the instructions:

  1. Reference Custom Agents: Use the agent drop-down in Copilot Chat to select a custom agent

  2. Learning Coaching:

  • Kata Coach: #file:/.github/agents/learning-kata-coach.agent.md for focused practice exercises
  • Lab Coach: #file:/.github/agents/learning-lab-coach.agent.md for complex training labs

  1. Enhanced Capabilities: Custom agents have comprehensive tool access for research, file editing, and system interaction

  2. Coaching Methodology: Follows OpenHack-style discovery-based learning with systematic guidance

Task Planning and Implementation

  • Task Planner Custom Agent: Access advanced planning capabilities through the hve-core VS Code extension

    • Creates structured development plans with phases and tasks
    • Performs research to gather context for comprehensive planning
    • Generates documentation in ./.copilot-tracking/plans/ (excluded from git)

  • Task Implementation Instructions: Enhance implementation with task-implementation.instructions.md context instructions

    • Provides guidance for executing plans and tracking progress
    • Works with task planning outputs for coordinated development flow
    • Follows standardized workflows for consistent implementation practices
    • When you select a file in the .copilot-tracking/plans/ directory, Copilot will automatically apply the task implementation instructions context

Learning AI Coaching Integration

Explore advanced AI-assisted engineering practices through our Learning Platform:

Interactive Learning Support

  • ✅ Task Check-offs: Mark progress and track learning automatically
  • 🆘 Coaching Hints: Get contextual help when stuck on exercises
  • 🧭 Smart Guidance: Personalized coaching based on your development patterns
  • 📊 Skill Assessment: AI-powered recommendations for your next learning steps

Getting Started with AI Coaching

  1. Launch Training Mode: Run npm run docs to access the learning platform
  2. Select Coaching Mode: Choose "Learning Kata Coach" in GitHub Copilot Chat
  3. Start Learning: Say "I'm working on learning and want interactive coaching"
  4. Get Personalized Path: Take the skill assessment for customized kata recommendations

All Learning coaching modes are pre-configured and ready to use immediately in this repository. All advanced agent prompts can be easily copied into your own project for immediate AI-assisted engineering acceleration.

Essential Project Prompts

Pull Request Generation (/pull-request)

  • Generates comprehensive PR descriptions following project standards
  • Ensures proper documentation updates and review checklist completion
  • Options: includeMarkdown=true, branch=feat/branch-name

Task Planning

  • Task Planner: Available through the hve-core VS Code extension
  • Files stored in ./.copilot-tracking/ (excluded from git)
  • Works with the task-implementation.instructions.md for enhanced guidance

Deployment Assistance (/deploy)

  • Provides deployment guidance and workflows specific to project blueprints
  • Infrastructure deployment assistance following project conventions

Architecture Decision Records

  • Guided ADR creation using the adr-creation custom agent
  • Ensures proper documentation of architectural decisions

Project Structure Integration

The AI resources are designed to work with the project's specific structure:

Component Development

  • AI understands the decimal naming convention (e.g., 000-cloud, 010-security-identity)
  • Recognizes internal modules and their scoping rules
  • Follows deployment patterns from CI directories and blueprints

Blueprint Creation

  • AI can suggest component combinations based on existing blueprints
  • Understands output-to-input mapping between components
  • Follows blueprint documentation requirements

Framework-Specific Guidance

  • Terraform: Module organization, variable patterns, testing with Terratest
  • Bicep: Parameter definitions, module structure, Azure resource patterns
  • C#: Testing standards, project structure, dependency patterns

GitHub Copilot for Azure Extension

When using the Dev Container, the GitHub Copilot for Azure (Preview) extension provides:

  • Azure-specific agents: Use #azure... tags for Azure-specific assistance
  • Resource schema: #azureBicepGetResourceSchema for latest Bicep schemas
  • Best practices: #azureTerraformBestPractices for Terraform guidance
  • Documentation: #azureRetrieveMsLearnDocumentations for up-to-date Azure docs

Additional Resources

For general GitHub Copilot usage, refer to the official documentation.

🤖 Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.