context-engineering

# Context Engineering Four operations control everything about how context flows through an AI coding session. Master them and you control the quality of every response. ## The Four Operations ### 1. Write — Persist Info Outside Context Move information out of the context window into durable storage so it survives compaction and session boundaries. **Where to write:** | Target | When | Example | |--------|------|---------| | CLAUDE.md | Permanent project rules | "Always use pnpm, never npm" | | NOTES.md / scratchpad | Working state for current task | Architecture decisions, open questions | | `.claude/memory/` | Learnings and patterns | `[LEARN]` rules from corrections | | External files | Data too large for context | Test plans, migration checklists | **Pattern — Scratchpad workflow:** ```text 1. Start complex task → create NOTES.md with goals and constraints 2. After research → write findings to NOTES.md 3. After compaction → NOTES.md survives, context does not 4. Resume → read NOTES.md to recover full state ``` ### 2. Select — Retrieve Relevant Info Pull the right information into context at the right time. Precision matters more than volume. **Methods ranked by precision:** 1. `@file` references — exact file injection 2. `grep` / `Glob` — targeted pattern search 3. Subagent exploration — delegated deep search 4. RAG / embeddings — semantic retrieval for large codebases **Key principle: Focused 300 tokens > unfocused 113K tokens.** A surgical grep result that ...