Context Window Optimization: Beyond Naive Truncation

The Truncation Problem

Most developers handle large contexts the same way: truncate to the last N tokens. This is fast and simple, but it throws away information indiscriminately.

What you lose with truncation:

Early context that establishes the problem domain

Function definitions referenced later in the code

Important constraints mentioned at the beginning of a document

A Better Approach: Semantic Compression

Instead of cutting from one end, semantic compression analyzes the entire document and keeps the most important parts regardless of position.

How It Works

Chunking — Split the document into semantic units (paragraphs, functions, sections)

Embedding — Generate vector representations of each chunk

Graph construction — Build a graph where edges represent semantic similarity

Importance scoring — Use PageRank to identify the most structurally important chunks

Skeleton extraction — Keep the top-ranked chunks, maintaining document order

The Key Insight

Documents have structure. A well-written technical document has:

Scaffolding — the logical structure that everything hangs on

Detail — examples, elaboration, edge cases

Redundancy — concepts restated in different ways

Compression removes detail and redundancy while preserving scaffolding. The LLM still understands the context because the skeleton carries the meaning.

Three Research Papers Behind Our Engine

We've implemented three state-of-the-art compression techniques:

STAE (Semantic-Temporal Aware Eviction) — centroid-temporal hybrid scoring for dialogue compression

SemToken — pre-processing that identifies and removes redundant spans before chunking

COMI — coarse-to-fine query-guided compression that focuses on query-relevant content

Together, these achieve 85%+ compression on typical documents while maintaining 90%+ semantic fidelity.

Try It Yourself

Paste any text into our playground and see the compression in action — no signup required.

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