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Context Architecture
AI product design is pivoting from simple prompt engineering to 'context architecture,' which treats the AI's information environment like an information architecture project.
What: Paz Perez at Nielsen Norman Group argues that as agents become autonomous, designers must use principles like hierarchy, taxonomy, and labeling to structure the system's memory, tools, and retrieval pipelines for better accuracy.
Why it matters: This signals that the bottleneck for reliable AI agents is no longer model capability, but the structural design of the information pipeline feeding those models.
Takeaway: When designing agentic systems, audit your tool definitions and knowledge base taxonomies; ensure they reflect the user's mental model rather than the internal engineering terminology of your APIs.
Deep dive
- Moving beyond static 'prompt engineering' to dynamic 'context architecture'.
- Applying classical Information Architecture (IA) principles to LLM ecosystems.
- Structuring retrieved knowledge to reduce retrieval 'noise' and model hallucinations.
- Designing categorization and labeling that mirrors user mental models to improve tool selection.
- Establishing clear retention and scoping rules for long-term agent memory.
- Managing information overload in the model's 'attention' window.
- Treating context design as a non-neutral UX task that actively shapes agent behavior.
Decoder
- Context Engineering: The practice of assembling instructions, retrieved data, tools, and memory into the AI's prompt window to influence model output.
- RAG (Retrieval-Augmented Generation): An architecture where the AI retrieves external information from a database to ground its answers before generating a response.
- MCP (Model Context Protocol): An open standard that allows AI models to connect to different external tools and data sources in a uniform way.
- Agentic System: An AI application that can perform tasks, use tools, and make decisions across multiple steps without constant human intervention.
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