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- AI
Generative AI Across the Software Development Lifecycle
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes:
- Introduction to GenAI in the Software Development Lifecycle - Applying GenAI to Planning and Requirements - Applying GenAI to Design and Architecture - Applying GenAI to Testing, QA, and Code Review - Applying GenAI to DevOps and Operations - Governing and Measuring GenAI Across the SDLC
Generative AI is transforming how software teams plan, design, build, test, and ship software, reshaping every phase of the development lifecycle rather than just the coding experience itself.
This path explores how to apply Gen AI tools across the full software development lifecycle, from requirements gathering and system design through testing, DevOps, and operations. You will learn practical, tool-agnostic techniques for integrating Gen AI into real delivery workflows, alongside the governance, measurement, and responsible use practices needed to adopt AI effectively at the team and organizational level.
Content in this path
Generative AI Across the Software Development Lifecycle
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What You'll Learn
- How to apply Gen AI tools to project planning, requirements gathering, and documentation to accelerate and improve the early stages of software delivery
- How to use Gen AI to support system design, architecture decision-making, API specification, and technical documentation
- How to leverage Gen AI for test strategy, test case generation, automated testing, code review, and quality assurance workflows
- How to apply Gen AI to CI/CD pipelines, infrastructure as code, deployment automation, monitoring, incident response, and cloud operations
- How to establish governance frameworks, manage compliance and IP risk, measure the impact of Gen AI adoption
- Learners should have a foundational understanding of the software development lifecycle and be familiar with at least one phase of software delivery (such as development, testing, or DevOps) in a professional context. Prior experience with AI tools is not required, though basic familiarity with large language models or generative AI concepts will help learners get the most out of this path.
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