AI

Context Windows

A practical guide to understanding what context windows are and why they matter for AI product design.

What a context window is, how it affects AI behaviour across a conversation, and what product and design teams need to account for when building AI features.

22 May 20264 min read

What it is

A window is the amount of text an can and consider at one time. It defines the model's effective working memory for a given conversation or task.

Everything the can see — the , the conversation history, any documents provided, and the current message — must fit within the context window. Anything outside it is invisible to the model.

windows are measured in tokens, which roughly correspond to words or parts of words. A context window of 100,000 tokens can hold roughly 75,000 words — a substantial amount, but still finite.

When a conversation exceeds the window, earlier content is dropped. The can no longer reference it, which can cause it to repeat itself, lose track of instructions, or appear to forget what was discussed.

Understanding windows helps you design AI that behave reliably across the full range of intended .

When to use it

Understand when window limits become a real constraint. They matter most when:

They matter less when:

Conversations or interactions are expected to be long
Documents or large amounts of content are passed to the model
The AI needs to reference earlier parts of a conversation accurately
System prompts are long and take up significant context space
Users will notice and be frustrated if the AI loses track
Interactions are short and self-contained
Each conversation starts fresh with no carry-over

Key takeaway

Context window limits are a design constraint, not just a technical one. Plan for them from the start rather than discovering them in testing.

How it works

Understand the basic mechanism. The window is a fixed-size buffer that holds all the text the can currently see. As a conversation grows, new content is added and — once the limit is reached — the oldest content is removed to make space.

This means the cannot look back beyond what fits in the window. It is not that the model has forgotten — the information is simply no longer present in what it can see.

Different have different window sizes. Larger windows allow for longer conversations and more context, but also increase the computational cost of each .

What this means for designers and product teams. Long conversations, lengthy documents, and large all consume space. Understanding this helps you make smarter decisions about what to include and when.

For that require memory across very long , additional — such as summarisation of earlier content or persistent storage — may be needed to supplement the context window.

What to look for

Focus on:

Conversation length — whether typical user interactions risk exceeding the context window
System prompt size — how much context space the prompt itself consumes
Document size — whether attached content takes up most of the available window
Context loss behaviour — how the AI behaves when earlier content is no longer in scope
User expectations — whether users expect continuity the system cannot reliably provide

Where it goes wrong

Most issues come from: Designing a conversational AI without accounting for limits will produce issues at scale.

System prompts that are too long and leave little space for the conversation
No mechanism for handling long conversations gracefully
Users expecting the AI to remember earlier interactions across sessions
Assuming larger context windows eliminate the problem entirely
Not testing at the edges of the context limit

What you get from it

Understanding windows gives you:

Clearer constraints for designing AI conversation features
A basis for decisions about memory, summarisation, and session design
More reliable AI behaviour across varied interaction lengths
Better conversations with engineers about architecture decisions

Key takeaway

Context windows define what the AI can see. Design your features around that constraint rather than against it.

FAQ

Common questions

A few practical answers to the questions that usually come up around this method.

What is a context window in AI?

It is the amount of text an can at one time. Everything the model can see — the conversation history, any documents provided, and the current message — must fit within this window. Content that exceeds it is no longer visible to the model.

Why does AI seem to forget things in long conversations?

Because the earlier parts of the conversation have moved outside the window. The has not forgotten — it simply cannot see content that is no longer within its working memory.

How big are context windows?

It varies by and is increasing over time. Many current models support windows of 100,000 tokens or more, which is roughly equivalent to a short novel. But even large windows have limits, and the more content you put in, the less reliably the model attends to all of it.

Can AI remember things between separate conversations?

Not through the window — it resets at the start of each new conversation. Persistent memory across requires additional , such as storing summaries or relevant details and injecting them into new conversations.

Should I always use the largest available context window?

Larger windows give more flexibility, but they are more expensive to run. For short, focused , a large context window offers no benefit. Match the context window size to the actual requirements of the you are building.

Quick take

If an AI seems to forget earlier parts of a conversation or loses track of what was said, the context window is usually why.

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Will Parkhouse

Senior Content Designer

01/20