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