The 7-day roadmap / day 7 of 7
Day 7: AI in the SDLC.
You'll see how AI-Native practices apply across the full lifecycle: from requirements and architecture through implementation, testing, review, and maintenance. This ties the week together and shows where to apply what you've learned.
- 7 topics
- 7 practice exercises
- 13 min of reading
- Free, no email
The 7 topics
- 01AI in Requirements and Product CollaborationHow engineers can use AI to help gather requirements, turn them into structured user stories, and validate acceptance criteria before a single line of code is written. The SDLC starts before implementation, and AI can make the engineering-to-product handoff much more structured and less lossy.TheoryPractice4 resources2 min
- 02AI as the Bridge Between Design and DevelopmentHow AI is closing the gap between what designers create and what engineers implement. Tools like Figma MCP, v0, and Builder.io Visual Copilot let engineers turn design files, screenshots, or prompts into production-ready component code, reducing the manual translation work that traditionally consumed hours every week.TheoryPractice5 resources2 min
- 03Testing with AIHow to use AI agents to write, run, and maintain tests, from unit tests to end-to-end browser flows. Covers AI-generated test cases, Playwright MCP for browser automation, and the BMAD TEA module for risk-based test strategy and release gates.TheoryPractice3 resources2 min
- 04AI for DocumentationUsing AI to generate and maintain documentation: code comments, API references, architecture docs, and user-facing guides. Documentation rot is one of the most persistent problems in software teams; AI tools are starting to solve it by coupling docs directly to code and updating them automatically.TheoryPractice5 resources2 min
- 05Continuous AI: AI in Your CI/CD PipelineHow AI fits into CI/CD pipelines beyond running tests. GitHub Agentic Workflows, using AI models inside GitHub Actions to auto-triage build failures, fix broken tests, synchronize docs with code changes, and handle repetitive repo tasks that previously required manual effort.TheoryPractice5 resources2 min
- 06AI for Debugging and Incident ResponseUsing AI to speed up root cause analysis, correlate logs with code changes, and investigate production incidents. Engineers spend a significant chunk of their time debugging; AI tools are already cutting that time by a meaningful amount in real production systems.TheoryPractice4 resources2 min
- 07AI for Your Custom Workflows in the SDLCFrom automating performance audits on a frontend codebase to generating security reports, triaging support tickets or keeping runbooks up to date, almost every task you do in the SDLC can benefit from AI in some way. The key is knowing how to identify those opportunities and structure your workflow to take advantage of them.TheoryPractice2 min
What you will watch and read
The roadmap is the shortest route to the ideas in AI-Native Software Engineering (O'Reilly Media). If you would rather work through them with your team, the canvas turns them into a workshop.