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 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 ¶

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# Service Operations Runbook: Payments API

## Purpose and scope

This runbook covers the payments-api service: what it does, how it is deployed, what its dependencies are, and what to do when it misbehaves. It is written for the on-call engineer. Every procedure here assumes you have production read access and the ability to trigger a deploy through the standard pipeline. Nothing in this document requires direct database write access, and no procedure here should be improvised under pressure: if the situation is not covered, page the service owner rather than inventing a fix at 3am.

The payments-api accepts charge requests from the checkout frontend, validates them against the pricing catalog, forwards them to the payment processor, and records the outcome in the orders database. It is the only service permitted to talk to the processor. Average traffic is steady during business hours with a daily peak around 19:00 UTC and a weekly peak on Friday evenings.

## Architecture and dependencies

The service runs as three replicas behind the regional load balancer. Each replica is stateless; all persistent state lives in the orders database and the idempotency-key store. The service depends on four things: the orders database (primary and one read replica), the idempotency-key store, the pricing catalog service, and the external payment processor. Of these, only the processor is outside our control.

Dependency failure behavior is deliberate and asymmetric. If the pricing catalog is unreachable, the service serves prices from its local cache for up to ten minutes and emits a degraded-mode metric. If the idempotency store is unreachable, the service refuses new charges entirely, because accepting a charge without idempotency protection risks double-billing, and double-billing is strictly worse than downtime. If the processor times out, the charge is recorded as pending and a reconciliation job resolves it within the hour.

## Deployment

Deploys go through the standard pipeline: merge to main, automated tests, staging deploy, a thirty-minute soak with synthetic checkout traffic, then production rollout one replica at a time. The pipeline aborts automatically if the error rate on the new replica exceeds the old baseline. A full rollout takes about twenty minutes. Rollback is the same pipeline in reverse and takes about six minutes; the on-call engineer can trigger it without approval.

## Monitoring and alerts

Three alerts page the on-call engineer. High charge failure rate fires when more than two percent of charge attempts fail over five minutes; the usual causes are a processor incident or a bad deploy, in that order. Idempotency store unavailable fires immediately on connection failure. Reconciliation backlog fires when pending charges older than ninety minutes accumulate, which usually means the reconciliation job is stuck rather than the processor being slow.

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Cite this¶

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BibTeXbibtex

@misc{context-window-optimization-2026,
  title  = {Context Window Optimization: Beyond Naive Truncation},
  author = {James Hollingsworth},
  year   = {2026},
  month  = {April},
  url    = {https://gotcontext.ai/blog/context-window-optimization},
  note   = {gotcontext.ai engineering blog.},
}

APAtext

James Hollingsworth. (2026, April 10). Context Window Optimization: Beyond Naive Truncation. gotcontext.ai. Retrieved from https://gotcontext.ai/blog/context-window-optimization.

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