Do Not Put the Whole Library on the Desk

~27 min · index, pointers, selective-loading

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The mature strategy

Real context work looks like a library, not a landfill. You keep an index of what exists, load only what the current task needs, and unload or summarize when the material stops being active. Even if a million tokens fit, you usually should not fill them.

Working set, not corpus

Operating systems do not keep every file in CPU cache. They page in the active working set. AI workflows should do the same: pointer to everything, full text only for what the current decision needs. The pointer is cheap (a path + summary + relevance hint); the full text is expensive in tokens, attention, and noise.

Index lifecycle

The index itself is a stable artifact: list of files, summaries, freshness, when to load. It belongs in the spine or in a project-instruction file the model reads at session start. If the index changes every turn, it is not an index — it is a working set.

Index first. Load second. Work third. Evict when done.

Code

Library-style context·yaml

context_library:
  index:
    - path
    - summary
    - owner
    - freshness
    - when_to_load
  active_working_set:
    - only files needed for the current decision
  evict:
    - stale logs
    - one-time command output
    - superseded drafts

Real index entry·yaml

- path: backend/auth.py
  summary: "OAuth + session cookie middleware"
  freshness: "current"
  when_to_load: "any task touching login, session, or auth headers"
- path: docs/legacy-auth-2024.md
  summary: "Pre-OAuth design (deprecated)"
  freshness: "reference only"
  when_to_load: "only when investigating historical decisions"

External links

Gemini long context guide

Exercise

Create a mini index for five project files. For each, write when it should be loaded and when it should stay as a pointer.

Hint

A schema file may be loaded often. A year-old history note may only need a pointer.

Progress

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