Prompt engineering tells AI what to do. Context engineering tells AI who it is.
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AI context management is the work of deciding what an AI system should know for a task, where that information lives, and how it is corrected when it changes. The five layers below separate stable instructions from working memory, connected tools, repeatable skills, and automatic checks.
| Layer | Use it for | Keep it useful by |
|---|---|---|
| CLAUDE.md | Stable instructions and routing | Pointing to the smallest relevant source instead of repeating everything. |
| Memory | Decisions, preferences, and lessons that survive a session | Recording source, owner, and the moment a fact was last confirmed. |
| MCP | Retrieval and actions across the tools where work happens | Keeping permissions and tool scope explicit. |
| Skills | Repeatable work with a defined input, output, and review | Updating the instruction when a correction repeats. |
| Hooks | Checks that run at the right point in a workflow | Making the trigger and expected evidence visible. |
When a question depends on relationships between sources, owners, commitments, and changes, govern AI context with evidence, freshness, and corrections. If you want this operating layer in your own work, build this context system in your Second Brain, then add a durable memory layer for the knowledge that needs to last.
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