# Personal Context Management (PCM) Personal Context Management is the practice of curating, structuring, maintaining, and evolving the entire context layer that ensures AI is as aligned as possible with you and your goals. It means making your personal knowledge system as easy to understand, use, and leverage for your AI agents as it is for yourself. The term was coined by [[Tiago Forte]]. PCM goes beyond crafting good prompts or even doing [[Context Engineering]] well. It encompasses the full lifecycle of managing your personal AI context: building it, maintaining it, reviewing it, and evolving it over time. [[Context Engineering]] is the key skill people need to develop right now; there's a ton of value at the personal level and at work. PCM is how that skill gets applied to your own life and knowledge. ## What PCM includes PCM is a combination of: - **[[AI Master Prompt|Master prompts]]**: rules, preferences, and behavioral guidelines for how AI should interact with you - **Identity context**: who you are, your values, beliefs, principles, goals, projects, and history - **Memory systems**: AI-managed memories that accumulate and evolve across conversations - **[[AI Agent Skills|Skills]]**: codified procedures for how you want specific tasks done - **[[AI Agents]]**: specialized agents with distinct identities, memories, and capabilities - **Knowledge base**: your [[Personal Knowledge Management (PKM)|PKM]] system reimagined as an AI-accessible context layer - **Rules and constraints**: what AI should and shouldn't do, how it should communicate ## The PCM insight Most people treat AI as stateless: every conversation starts from zero, and they provide minimal context. PCM recognizes that **the quality of AI output is directly proportional to how well AI understands your full context**: your past, present, and future. This is the same principle behind [[Levels of AI Context Management]]: progressing from no context (Level 1) to an AI-ready knowledge system (Level 8) where AI agents fully understand you and actively enrich your knowledge base over time. ## PCM as a lifecycle PCM is not a one-time setup. It's an ongoing practice: 1. **Build**: create your master prompts, identity notes, skills, and agent configurations 2. **Maintain**: keep context current as your goals, projects, and knowledge evolve 3. **Review**: periodically audit whether AI's understanding of you is still accurate 4. **Evolve**: add new skills, refine agents, update rules as your needs change ## Relationship to PKM PCM builds on [[Personal Knowledge Management (PKM)|PKM]] but extends it. Traditional PKM is designed for human retrieval and thinking. PCM ensures the same knowledge system is also optimized for AI consumption. Your second brain becomes your AI's brain too. This is what [[Agentic Knowledge Management (AKM)]] looks like in practice at the individual level. ## The context management hierarchy PCM is the individual layer in a nested hierarchy. All levels use the same principles and techniques, just applied at different scales: - **[[Enterprise Context Management (ECM)|ECM]]**: organization-wide context governance and policies (strategy, focus, culture) - **[[Team Context Management (TCM)|TCM]]**: team-level shared context (members, processes, priorities) - **[[Project Context Management (PCM)|Project Context Management]]**: project-specific context (architecture, design, implementation details, business rules) - **[[Personal Context Management (PCM)|PCM]]**: individual preferences, style, and personal knowledge Each layer inherits from the level above and adds its own context. In an organization, [[Context Inheritance]] flows down: project context takes into account enterprise context and team/service/department context. Individuals inherit team and project standards and layer their own on top. ## References - ## Related - [[Context Engineering]] - [[Prompt Engineering]] - [[AI Master Prompt]] - [[How to create your Personal AI Master Prompt]] - [[Levels of AI Context Management]] - [[Levels of AI use]] - [[Personal Knowledge Management (PKM)]] - [[Agentic Knowledge Management (AKM)]] - [[AI Agents]] - [[AI Agent Skills]] - [[AI Agent Memory]] - [[AI Agent Identity]] - [[AI Assistants]] - [[Project Context Management (PCM)|Project Context Management]] - [[Team Context Management (TCM)]] - [[Enterprise Context Management (ECM)]] - [[Tiago Forte]] - [[Harness Engineering]] - [[Intent Engineering]] - [[Context Hygiene]] - [[Context Drift]] - [[AI context is finite with diminishing returns]] - [[Claude Code]] - [[Context Lifecycle]] - [[Context Layering]] - [[Context Inheritance]] - [[Context Budget]] - [[Context Signal-to-Noise Ratio]] - [[Context-as-Code]] - [[Agentic Context Engineering]] - [[Context Management Maturity Model]]