Context Architecture for AI systems

Key Insights

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  Context is a critical lever in AI product behavior, equally important as the underlying model.

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  Information architecture principles like hierarchy, categorization, and labeling directly improve AI outputs.

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  More context data is not always better—excess context can cause model inconsistency, hallucinations, and increased costs.

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  Memory management in AI is unresolved but essential to reduce hallucinations and maintain relevant user history.

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  Skills—reusable markdown files describing tasks—are a new and popular form of context but require clear naming and organization for usability.

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  Evaluation is now vital for AI products to continuously assess model behavior, context effectiveness, and cost efficiency.

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  Designers, especially content designers, information architects, and service designers, bring unique value to AI product teams.

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  Narrower, domain-specific AI models are emerging as safer, more controllable, and more effective alternatives to large general models.

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  Collaboration between engineers and designers around taxonomy and context can reduce token usage and improve product efficiency.

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  AI agents with different levels of autonomy need more robust context and evaluation to ensure safe and accurate behavior.

Notable Quotes