---
title: Managing Small Context Windows in Language Models
url: https://www.dataloco.com/en/managing-small-context-windows-in-language-models
published: 2026-09-17T02:11:05+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/managing-small-context-windows-in-language-models/
publisher: Dataloco
---

# Managing Small Context Windows in Language Models

An article discusses three practical strategies for managing small context windows in large language models. The focus is on how these strategies can lead to better performance in real-world applications while addressing various limitations associated with large context windows.

The first strategy highlighted is context truncation using a sliding window approach. This method treats user conversations as a FIFO queue, where the oldest interactions are dropped as new ones come in. Defining the size of the context window is crucial to maintain a balance between retaining sufficient past context and controlling latency and costs.

The sliding window approach provides predictability over token usage and computing overhead. By fixing the number of interactions the model processes at a time, it helps keep latency stable and manageable. The article suggests using Python code examples to illustrate how adjusting the context window size influences the model's memory.

The second strategy involves token budgeting and Retrieval-Augmented Generation (RAG) systems. RAG systems enhance large language models by retrieving external documents to provide relevant context. Token budgeting divides the context window into zones with specific limits, ensuring that only pertinent information is included. This prevents large retrieved documents from overwhelming the prompt.

Finally, the article briefly outlines other strategies for managing small context windows, emphasizing that these should be seen as design features that help mitigate issues like high costs and latency. The strategies presented aim to enable faster and more cost-effective solutions without sacrificing accuracy.