Every AI model has a finite context window. Designing within this constraint — and designing the user experience around it — is a core skill for AI product design.

The Context Window as a Design Material

The context window is not just a technical limitation. It's a design material:

What goes in: System prompts, conversation history, retrieved documents, tool results, user preferences

What gets dropped: Older messages, less relevant context, verbose instructions

What the user sees: The conversation as presented may differ from what the model actually processes Designers must understand context window allocation to design reliable experiences.

Memory and Persistence

Users expect AI to remember. Design for different memory horizons:

Within-conversation memory: What was said earlier in this chat. Usually handled by the context window itself.

Cross-conversation memory: Preferences, past decisions, ongoing projects. Requires explicit memory systems.

Shared memory: Context shared across multiple users or agents. Requires careful privacy design.

Strategies for Limited Context

Summarisation: Compress earlier conversation into summaries to free up tokens

Retrieval-augmented generation: Pull in relevant context on demand rather than keeping everything loaded

Priority ordering: Put the most important context closest to the prompt (recency bias in attention)

User-controlled context: Let users pin, remove, or prioritise what the AI remembers

Graceful degradation: When context is lost, acknowledge it rather than hallucinating continuity

Design Artefacts

Context budget allocations (how many tokens for system prompt, history, retrieval, etc.)

Memory architecture diagrams showing what persists and what's ephemeral

Context overflow UX flows (what happens when the window fills up)

User-facing memory controls specification

name	context-window-design
description	Designing around token limits, memory, and conversation persistence.