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12Factor.me - Four Phases × Twelve Principles Methodology
Source: https://www.12factor.me/
Methodology for 10x engineering efficiency improvement in the AI collaboration era
Phase 1: Preparation
Establish clear information architecture and context environment
1. Single Source of Truth
Core Concept: Scattered information leads to context confusion, easily causing misjudgment by both humans and machines.
Recommended Practices:
- Centralize all requirements, designs, and context in a unified document center (e.g., Notion / Confluence / GitHub Wiki).
- When collaborating with AI, directly reference this "source of truth" rather than randomly copying and pasting information.
Anti-patterns:
- Team members each maintain different versions of documents, leading to inconsistent AI responses and suggestions.
2. Prompt First
Core Concept: Treat prompts as the new generation of design documents.
Recommended Practices:
- Before starting a task, prioritize writing prompts to clarify inputs, outputs, styles, and constraints.
- Reuse validated and optimized prompt templates within the team.
Anti-patterns:
- Directly asking AI to write code without planning, leading to wrong direction and unnecessary rework.
3. Context Hygiene
Core Concept: Clean context enables more precise AI responses.
Recommended Practices:
- Start a new session for each new task to avoid old content interference
- Regularly summarize the current situation in one sentence to help AI "align context"
Anti-patterns:
- Mixing conversations from three days ago with today's tasks
Phase 2: Execution
Efficiently collaborate to complete specific tasks
4. Human-in-the-Loop
Core Concept: AI produces fast, but only humans can grasp direction and business judgment.
Recommended Practices:
- AI provides initial drafts, humans responsible for key decisions and risk control
- For important features, perform logic verification before merging code
Anti-patterns:
- Accepting AI output wholesale without any review
5. Chunked Work
Core Concept: Break large tasks into small chunks, easier to iterate and correct.
Recommended Practices:
- Keep tasks completable within 10-30 minutes
- Verify results immediately after each chunk
Anti-patterns:
- Having AI write 5000 lines at once, impossible to debug
6. Parallel Flow
Core Concept: While AI works, humans do low-context-switch side tasks to maintain rhythm.
Recommended Practices:
- Prepare a "side task list" including document organization, small fixes, code reviews, etc.
- While waiting for AI, don't take on high cognitive load new tasks to avoid excessive switching costs
Anti-patterns:
- Scrolling social media while waiting for AI, breaking the rhythm
Phase 3: Collaboration
Manage cognitive load and workflow during collaboration
7. Cognitive Load Budget
Core Concept: Human attention is a scarce resource.
Recommended Practices:
- Set daily time limits for AI collaboration
- Schedule deep review tasks during peak mental periods
Anti-patterns:
- Working with AI all day, completely exhausted by evening
8. Flow Protection
Core Concept: Once high-focus flow is interrupted, recovery cost is extremely high.
Recommended Practices:
- Set focus periods (e.g., 90 minutes), block notifications and interruptions
- AI interactions also done in batches during focus flow, not scattered triggers
Anti-patterns:
- Writing code while replying to messages while watching AI output, cliff-like efficiency drop
9. Reproducible Sessions
Core Concept: Collaboration process must be traceable for continuous optimization.
Recommended Practices:
- Save prompts, AI versions, change reasons to codebase or knowledge base
- When bugs occur, can replay the generation process
Anti-patterns:
- No record of AI generation history, can't trace causes when errors occur
Phase 4: Iteration
Continuous learning and improving collaboration patterns
10. Rest & Reflection
Core Concept: Retrospect after sprints to run faster.
Recommended Practices:
- After sprint ends, spend 5 minutes reflecting on AI output vs expectations
- Update prompt templates, accumulate "pitfall records"
Anti-patterns:
- Continuous sprints, accumulating errors without summary
11. Skill Parity
Core Concept: AI is a magnifier, amplifying abilities and also weaknesses.
Recommended Practices:
- Continuously learn domain knowledge and code review skills
- Maintain independent judgment on AI output
Anti-patterns:
- Completely relying on AI, losing manual skills and technical insight
12. Culture of Curiosity
Core Concept: Curiosity drives exploration, avoiding "blind trust in AI".
Recommended Practices:
- When facing AI answers, first ask "why", then ask "can it be better"
- Team shares AI usage experiences and improvement ideas
Anti-patterns:
- Accepting AI solutions without question
Generated from 12Factor.me License: MIT