Context Window Management

Context window management

Context is a budget, and everything competes for it: instructions, history, retrieved documents, tool results. Manage it like memory in an embedded system: allocated by purpose, compacted under pressure, with the invariant that the most decision-relevant tokens survive.

Method

1. Partition the budget explicitly. Fixed allocation for the system prompt (keep it lean: every instruction competes with data for attention), a reserve for the response, and the remainder split between history and retrieval by the application's shape (chat leans history; Q&A leans retrieval: see rag-pipeline). Know your model's real window and your p95 usage against it: overflow failures are a capacity-planning problem (see capacity-planning).
2. Compact history hierarchically, not by truncation. Rolling summarization: old turns compress into a summary block (decisions made, facts established, current goal), recent turns stay verbatim: the summary is regenerated as turns age past a threshold. Naive head-truncation deletes the instructions and early decisions that the conversation still depends on (the mid-conversation amnesia bug).
3. Preserve the load-bearing tokens. Some content must never compact away: the task definition, hard constraints, user identity/preferences, schema definitions for structured output (see structured-output), and safety instructions. Pin them (re-inject after compaction) rather than trusting the summarizer to keep them (see agent-memory for durable cross-session state; this skill is within-session).
4. Trim retrieval to relevance, not availability. Rank retrieved chunks, take top-k under the retrieval budget, and prefer many small relevant chunks over few large loose ones (see embeddings-selection's chunking interplay); deduplicate near-identical chunks (retrieval loves returning five copies of the same paragraph); summarize tool results that are data dumps before they enter context (a 50KB JSON response is usually 500 tokens of decision-relevant content wearing a payload).
5. Watch position effects. Models attend best to the start and end of context: instructions up front, the immediate question and freshest data near the end, the middle for reference material: and verify with needle tests on your actual layout when correctness depends on mid-context content (long-context claims degrade under load; measure, do not trust the spec sheet: see llm-eval-design).
6. Instrument token flows in production. Per-request token counts by section (system/history/retrieval/ response), compaction frequency, and overflow events (see llm-observability): rising history share means the compaction threshold needs tuning; cost tracks tokens linearly, so this dashboard is also the bill (see llm-cost-latency).

Boundaries

• Bigger windows are not a strategy: cost scales with tokens, latency grows, and attention quality drops before the hard limit; compaction discipline pays at every window size.
• Summaries are lossy by design; applications where exact history matters (legal, medical, audit) need verbatim logs outside the context (see audit-logging) with retrieval back in, not longer summaries.
• Prompt caching changes the economics of stable prefixes (see llm-cost-latency) and rewards a fixed system-prompt layout; design the partition with caching in mind from the start.