Designing for Context Window Limits

Why are context window limits an epistemological problem?

Deciding what fits in a context window is deciding what the model gets to know, and that curation, whether deliberate or accidental, determines the boundaries of what the model can reason about.

A 128K token context window sounds large until you try to fit a 200-page contract, a 50-email thread, and the relevant company policies into it. At that point, the question is no longer “how do I fit more in?” It is “what should I leave out?” That question is epistemological. It asks what information is necessary for sound reasoning and what can be safely excluded without distorting the conclusion.

I have built 6 production systems where context window management was a primary design constraint. In each case, the chunking and compression strategies were not performance optimizations. They were editorial decisions with direct impact on output quality. A summarization system that compressed a legal brief too aggressively lost the qualifying clauses that changed the meaning of key provisions. A research assistant that truncated search results by position rather than relevance consistently missed the most relevant papers because they appeared lower in the retrieval ranking.

What chunking strategies actually work in production?

The 3 chunking strategies that survive production deployment are semantic boundary chunking (splitting at natural topic transitions), recursive hierarchical chunking (multiple granularity levels), and late chunking (embedding full documents first, then extracting relevant segments).

Fixed-size chunking (512 tokens with 64-token overlap) is the most common approach and the least effective. It splits mid-sentence, separates cause from effect, and fragments arguments across chunk boundaries. In one evaluation, switching from fixed-size to semantic boundary chunking improved retrieval precision by 18 percentage points on a legal document corpus.

Semantic boundary chunking uses structural cues (headers, paragraph breaks, topic shifts detected by embedding similarity) to find natural split points. Recursive hierarchical chunking creates chunks at multiple levels (document, section, paragraph) and retrieves at the appropriate level based on query specificity. Late chunking, a newer technique, generates embeddings for the full document context then extracts chunk-level representations that preserve surrounding context. Each has tradeoffs in complexity, latency, and storage cost. The right choice depends on document structure and query patterns, not on which blog post you read most recently.

Is context compression a viable alternative to better chunking?

Context compression (reducing the token count of retrieved information while preserving its semantic content) can recover 40-60% of context window capacity, but introduces a quantifiable information loss that must be measured against the task’s precision requirements.

I have tested 3 compression approaches: extractive (selecting key sentences), abstractive (rewriting concisely via a smaller model), and token-level (using techniques like LLMLingua that prune low-information tokens). Extractive compression preserved 89% of factual accuracy while reducing token count by 45%. Abstractive preserved 82% at 55% reduction. Token-level preserved 91% at 38% reduction. The right choice depends on whether you need the original language (legal, regulatory) or just the information content (analysis, synthesis).

The tension remains: every compression step is an information loss step. The question for system designers is not “can I compress?” but “can I afford the specific information that compression removes?” That question has no general answer. It must be evaluated empirically for each domain, each task type, and each quality threshold. The work of managing context windows is, in the end, the work of knowing what matters.