Phase 1: Requirements Analysis
Goal: Generate user stories and technical specs.
Workflow:
Use LLM Node (e.g., GPT-4) to analyze the business scenario input (e.g., "Recommendation engine for e-commerce").
Prompt: "Generate user stories, non-functional requirements, and risks for [scenario]."
Save output to a Google Doc/Confluence (via n8n’s Google Drive/Atlassian node).
Send a Slack/Email Notification to stakeholders for review.
Human-in-the-Loop: Stakeholders approve/reject in Slack (using n8n’s Slack interactive message trigger).
Tools:
OpenAI API, Confluence, Slack.
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Phase 2: System Design
Goal: Create architecture diagrams and DB schemas.
Workflow:
Use LLM Node to convert requirements into a system design.
Prompt: "Design a microservices architecture for a recommendation engine using AWS."
Generate UML diagrams with Draw.io (.drawio XML files) or Mermaid.js (text-based diagram syntax).
Store design artifacts in GitHub Repo.
Human-in-the-Loop: Architect reviews and approves via a web form (n8n Form Trigger).
Tools:
GPT-4, Mermaid.js/Draw.io, GitHub.
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Phase 3: Development
Goal: Auto-generate code snippets and modules.
Workflow:
Use Code Generation Tools (e.g., GitHub Copilot via API) to scaffold code.
Example: "Generate Python code for a collaborative filtering recommendation model."
Save code to GitHub Repo (n8n GitHub node).
Run SonarQube (via API) for code quality checks.
Human-in-the-Loop: Developer reviews code in GitHub and approves via GitHub Issues.
Tools:
GitHub Copilot, SonarQube, Git.
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Phase 4: Testing
Goal: Automate test cases and bug detection.
Workflow:
Use LLM Node to generate test cases (e.g., "Write Jest tests for a recommendation API").
Store test scripts (e.g., tests/unit, tests/e2e) in the GitHub repository.
Execute tests with Jest (unit/integration testing) and Selenium (end-to-end/browser testing) via n8n’s CLI node.
Report bugs to Jira/Linear (n8n Jira node).
Human-in-the-Loop: QA Engineer reviews test results and approves via Jira.
Tools:
Jest, Selenium, Jira.
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Phase 5: Deployment
Goal: CI/CD pipeline with monitoring.
Workflow:
Trigger GitHub Actions/Docker build (n8n webhook).
Deploy to AWS ECS/Kubernetes (n8n AWS node).
Monitor with Datadog/Prometheus (alerting via n8n HTTP node).
Human-in-the-Loop: DevOps engineer validates deployment via Slack approval.
Tools:
Docker, AWS, Datadog.
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