---
title: "Context Window Expansion"
description: "Context window expansion is the set of techniques used to increase how much input a model can process in one request. It extends the context window so the model can receive longer documents, larger codebases, longer conversations, or more tool results."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/context-window-expansion"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

Context window expansion is the set of techniques used to increase how much input a model can process in one request. It extends the [context window](https://promptmetheus.com/resources/llm-knowledge-base/context-window) so the model can receive longer documents, larger codebases, longer conversations, or more tool results.

Expansion can happen through several approaches:

- training or fine-tuning on longer sequences;
- changing positional encodings;
- modifying attention mechanisms;
- using recurrent, memory, or state-space components;
- compressing older context into learned or explicit summaries; and
- retrieval systems that simulate larger context by selecting relevant chunks.

Longer context is useful, but it is not a substitute for [context engineering](https://promptmetheus.com/resources/llm-knowledge-base/context-engineering). A larger window can still contain irrelevant, contradictory, or poorly ordered information. It may also increase cost, latency, and exposure to [prompt injection attacks](https://promptmetheus.com/resources/llm-knowledge-base/prompt-injection-attack).

The practical question is the effective context window: how much information the model can use reliably for a task, not merely the advertised token limit.

The IJCAI survey [Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models](https://www.ijcai.org/proceedings/2024/917) reviews methods for extending LLM sequence length.

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