Model Context Management

Benched.ai Editorial Team

Model context management is the discipline of deciding which tokens enter a model's context window to maximize answer quality while respecting size and cost limits.

Techniques

Technique	What It Does	Best For
Truncation (head/tail)	Drop oldest or least recent tokens	Short chats
Summarization	Replace blocks with concise summary	Long support threads
Retrieval (RAG)	Insert top-k relevant chunks	Knowledge bases
Compression (token pruning)	Remove stop-words, whitespace	Pre-processing pipes
Slot reservation	Reserve N tokens for policy prompts	Safety & alignment

Token Budget Allocation Example (4 k window)

Segment	Tokens
System / safety	400
History summary	300
Retrieval evidence	1200
User question	200
Assistant answer (budget)	1900

Design Trade-offs

Heavy summarization may lose factual grounding.
Over-retrieval pushes user prompt out of window (lost-in-the-middle).
Slot reservation reduces available budget but prevents policy truncation.

Current Trends (2025)

Attention re-weighting models learn to ignore filler tokens, easing budget pressure.
Dynamic window allocators adjust segment sizes based on real-time entropy estimates.
Context auditing tools log token attribution for each generated word.

Implementation Tips

Apply "summary then truncate" to keep recent and important info.
Monitor average tokens/segment to catch regressions.
Store raw and processed context for offline QA.