# 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.

- **Source:** https://ainativesoftware.engineering/roadmap/day-7
- **Site:** AI-Native Software Engineering — https://ainativesoftware.engineering/book

- **Day:** 7 of 7
- **Topics:** 7
- **Reading time:** 13 minutes

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.

## The 7 topics

- 01 · [AI in Requirements and Product Collaboration](https://ainativesoftware.engineering/roadmap/day-7/ai-in-requirements-and-product-collaboration.md) — How 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. _(2 min)_
- 02 · [AI as the Bridge Between Design and Development](https://ainativesoftware.engineering/roadmap/day-7/ai-as-the-bridge-between-design-and-development.md) — How 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. _(2 min)_
- 03 · [Testing with AI](https://ainativesoftware.engineering/roadmap/day-7/testing-with-ai.md) — How 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. _(2 min)_
- 04 · [AI for Documentation](https://ainativesoftware.engineering/roadmap/day-7/ai-for-documentation.md) — Using 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. _(2 min)_
- 05 · [Continuous AI: AI in Your CI/CD Pipeline](https://ainativesoftware.engineering/roadmap/day-7/continuous-ai-ai-in-your-cicd-pipeline.md) — How 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. _(2 min)_
- 06 · [AI for Debugging and Incident Response](https://ainativesoftware.engineering/roadmap/day-7/ai-for-debugging-and-incident-response.md) — Using 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. _(2 min)_
- 07 · [AI for Your Custom Workflows in the SDLC](https://ainativesoftware.engineering/roadmap/day-7/ai-for-your-custom-workflows-in-the-sdlc.md) — From 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. _(2 min)_

**Every curated resource in day 7**

- [How to Use GenAI for Requirements Gathering and Agile User Stories – InfoWorld](https://www.infoworld.com/article/3980319/how-to-use-genai-for-requirements-gathering-and-agile-user-stories.html)
- [How to Use AI for Product Discovery and Better User Stories – Mountain Goat Software](https://www.mountaingoatsoftware.com/blog/user-story-ai-prompt-pack)
- [Can LLMs Generate User Stories and Assess Their Quality? (arXiv)](https://arxiv.org/html/2507.15157v1)
- [BMAD PM and Analyst Workflows – Official Docs](https://docs.bmad-method.org/)
- [Figma MCP Server – Official Developer Docs](https://developers.figma.com/docs/figma-mcp-server)
- [Figma Code Connect – Linking Design Components to Your Codebase](https://developers.figma.com/docs/code-connect/)
- [v0 by Vercel – AI UI Generation Docs](https://v0.app/docs)
- [The Complete Design-to-Code Guide – Builder.io](https://builder.io/blog/figma-design-to-code-guide)
- [Figma Make – Prompt to Interactive Prototype](https://figma.com/solutions/ai-design-generator)
- [Playwright MCP Explained: AI-Powered Test Automation in 2026](https://www.testleaf.com/blog/playwright-mcp-ai-test-automation-2026/)
- [The Complete Playwright End-to-End Story, Tools, AI, and Real-World Workflows – Microsoft](https://developer.microsoft.com/blog/the-complete-playwright-end-to-end-story-tools-ai-and-real-world-workflows)
- [BMAD TEA Module – Test Architect for Risk-Based Test Strategy](https://github.com/bmad-code-org/bmad-method-test-architecture-enterprise)
- [AutoDoc: Up-to-Date Documentation, Zero Effort – Cosine](https://cosine.sh/blog/autodoc-ai-coding-documentation)
- [Swimm – Continuous Documentation Coupled to Code](https://swimm.io/)
- [Mintlify – AI-Generated API Documentation](https://mintlify.com/)
- [AI-Driven Documentation in 2026 – Overcast Blog](https://overcast.blog/ai-driven-documentation-in-2026-f993f0c6d0d6)
- [Best AI Documentation Generators in 2026 – NxCode](https://nxcode.io/resources/news/ai-documentation-generator-2025)
- [Continuous AI in Practice – GitHub Blog](https://github.blog/ai-and-ml/generative-ai/continuous-ai-in-practice-what-developers-can-automate-today-with-agentic-ci/)
- [GitHub Agentic Workflows (Technical Preview) – GitHub Blog](https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows/)
- [Automate Your Project with GitHub Models in Actions – GitHub Blog](https://github.blog/ai-and-ml/generative-ai/automate-your-project-with-github-models-in-actions/)
- [Codex GitHub Action – OpenAI](https://developers.openai.com/codex/github-action/)
- [Codeflash – AI Performance Optimizer via GitHub Actions](https://docs.codeflash.ai/)
- [A Tale of Two Incident Responses: AI Found the Root Cause 3.5x Faster – Grafana Labs](https://grafana.com/blog/2025/11/17/a-tale-of-two-incident-responses-how-our-ai-assist-helped-us-find-the-cause-3-5x-faster/)
- [Speeding Up Root Cause Analysis with AI – Splunk](https://lantern.splunk.com/Observability_Use_Cases/Troubleshoot/Speeding_up_root_cause_analysis_with_artificial_intelligence)
- [Debug with GitHub Copilot in VS Code – Microsoft Docs](https://code.visualstudio.com/docs/copilot/guides/debug-with-copilot)
- [DebugMate: An AI Agent for Efficient On-Call Debugging – Springer](https://link.springer.com/article/10.1007/s44248-025-00074-y)

- **Previous day:** [Day 6: Spec-Driven Development](https://ainativesoftware.engineering/roadmap/day-6.md)
- **All days:** [The 7-day roadmap](https://ainativesoftware.engineering/roadmap.md)

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_AI-Native Software Engineering by Alfonso Graziano (O'Reilly Media, Early Release; print edition February 2027). Every page of ainativesoftware.engineering is also served as Markdown: append `.md` to any URL. Index: https://ainativesoftware.engineering/llms.txt — whole site in one file: https://ainativesoftware.engineering/llms-full.txt._