context-window-engineering

name	context-window-engineering
description	Decide what actually goes in the context window — static instructions vs just-in-time retrieval vs conversation history vs tools — with a per-section token budget and lost-in-the-middle-aware ordering. Reach for this when the window is bloating, quality is dropping as context grows, cost/latency is climbing, or you're unsure what to keep vs compress. Pairs with prompt-pattern-selection.

Skill: Context-window engineering

The window is a budget, not a scratchpad. This skill decides what earns its tokens and in what order.

Step 0 — One opinion up front

More context is not free, and past a point it hurts. Cost and latency scale with fill, and accuracy can drop (the lost-in-the-middle effect + dilution). If quality falls as you add context, remove context.

Step 1 — Classify every candidate

For each thing you're tempted to include, classify it (trace ../../knowledge/prompt-decision-trees.md §3):

• Needed every call → static system context (instructions, schema, invariants, few-shot).
• Query-dependent knowledge → retrieve just-in-time (ai-rag-engineering), budgeted.
• Conversation history → keep recent verbatim; summarize/compress older turns.
• Everything else → probably doesn't belong.

Step 2 — Budget tokens per section

Assign a token ceiling to each section (system, retrieved, history, tools, output headroom). The sum must leave room for the response. Write the budget down.

Step 3 — Order for the model, and for the cache

• Lost-in-the-middle: put the decisive material at the start or end, not buried in the middle.
• Prompt caching: put stable content first (system, schema, few-shot) so it caches; put variable content last. This is a cost and latency lever.

Step 4 — Define eviction order

When the window approaches its limit, what gets dropped or compressed first? (Usually: oldest history → least-relevant retrieved chunks → verbose examples.) Make it a rule, not an accident.

Step 5 — Hand off

• The retrieval that fills a query-dependent slot → ai-rag-engineering.
• The model's window size / caching behavior → ai-coding-model-guidance / claude-api.
• Measuring whether a context change helped → prompt-reliability-engineer.

Output

A context-inclusion plan: each section classified, a per-section token budget, the ordering (lost-in-the-middle + cache-aware), and the eviction order.