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:

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 returns the exact function signature beats dumping an entire module into context. Every irrelevant token dilutes attention.

Pattern, Progressive retrieval:

1. Start with file names (Glob)
2. Narrow to specific functions (Grep)
3. Read only the relevant lines (Read with offset+limit)
4. Never read entire large files when you need one function

3. Compress, Reduce Tokens, Preserve Signal

Shrink context without losing the information that matters.