Context Window Design
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