.NET Developer Productivity with AI: Copilots, Automation, and Intelligent Workflows
Description
This training equips .NET developers with the skills to leverage Generative AI tools and copilots to significantly enhance productivity across the software development lifecycle. Participants will explore how AI-assisted coding tools such as GitHub Copilot integrate with Visual Studio to accelerate development, improve code quality, and reduce repetitive tasks. The course covers intelligent code generation, refactoring, testing automation, and documentation using AI. It also introduces the design of AI-powered workflows within .NET applications, including integration with LLMs, APIs, and orchestration layers. Hands-on labs demonstrate how to build intelligent features such as natural language interfaces, automated decision services, and smart assistants within enterprise applications. Emphasis is placed on maintainability, security, and responsible AI usage in production environments. By the end of the training, participants will be able to augment their development processes and applications with AI-driven capabilities.
Indicative Duration: 18 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
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1. AI-Augmented
Development |
1.1 Foundations of AI in Development | • Overview of AI in software engineering • Productivity impact • Introduction to GitHub Copilot |
| 1.2 Code Generation Techniques | • Writing effective prompts/comments • Generating methods, APIs, refactoring patterns using AI |
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2. AI-Assisted Coding
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2.1 Hands-on ASP.NET Core Development | • Building Web API endpoints using AI assistance • Improving code structure and speed |
| 2.2 Code Understanding & Bug Detection |
• Explaining code • Identifying issues • Root cause analysis using AI tools |
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| 3. Documentation Automation |
3.1 AI-Generated Documentation | • Creating API documentation • Summarizing codebases • Generating technical explanations |
| 4. Testing with AI | 4.1 Automated Test Generation |
• Generating unit and integration tests • Identifying edge cases • Improving coverage |
| 5. Developer Copilots | 5.1 Building Internal AI Tools | • Creating developer assistants using Semantic Kernel • Plugin-based tooling |
| 6. Security in AI Development |
6.1 Responsible AI Usage | • Risks (code leakage, prompt injection) • Secure usage patterns and guidelines |
| 7. Optimization & Best Practices | 7.1 Efficient AI Usage | • Prompt optimization • When to use AI vs manual coding • Reducing noise and cost |
Learning Objectives
Upon completion of the course participants will be able to:
- Use AI copilots to accelerate coding, debugging, and refactoring in .NET environments
- Automate testing, documentation, and code generation workflows using AI tools
- Integrate LLM capabilities into .NET applications via APIs and SDKs
- Design intelligent application features such as assistants and decision-support components
- Apply best practices for secure, maintainable, and production-ready AI-augmented systems
Target Audience
- Roles: .NET Developer, Software Engineer, Technical Lead
- Seniority: Mid to Senior Level
Prerequisite Knowledge
- Solid experience with C# and the .NET ecosystem including Visual Studio and basic application architecture
- Familiarity with REST APIs and general software development lifecycle practices
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
