information architecture

'How you hold' compute is fundamentally about structuring information efficiently—the core of information architecture for AI systems

Core insight is that *what you include* in context matters as much as *how* you phrase it. Removing model names is an information architecture decision.

Organizing files in folders, naming everything correctly, introducing things in the right order" — Article exemplifies information architecture in action: hierarchical folder organization, semantic na

Tree-indexed document structure is fundamentally about how to organize information for LLM reasoning

MCP requires explicit structuring of what information/tools are available to AI, enforcing clarity about problem scope and available context

Article frames context engineering as architectural question - 'what configuration of context' - not tactical prompt tweaking

Context engineering is reframed as information architecture problem—which information is active, archived, summarized, or offloaded. This is design, not prompting.

The 3-layer framework (Functional/Visual/Data) is an information architecture pattern applied to AI context

Author's confusion about Obsidian's role is fundamentally an information architecture problem—each tool should have clear ownership of what information flows through it

Context engineering is specific instance of broader information architecture discipline—how to structure, prioritize, and preserve information for system effectiveness.

Article implies that sequencing, prioritization, and placement of information in context is distinct from content quality—suggesting information architecture is a context engineering discipline.

Suggests MCP as infrastructure for information architecture at ecosystem level, not just application level

Article emphasizes that organizing information and designing processes is the core skill, which is information architecture applied to AI systems.

Core insight is about how information is organized. Poor organization makes retrieval slow/expensive; good organization enables both speed and correctness.

Article implicitly argues that what information is available to AI system is architecturally important—classic information architecture problem

Author discusses organizing requirements, specifications, and constraints hierarchically to support AI code generation—directly related to how information is structured for AI consumption.

The 'decision chains' pattern requires deliberate information architecture so each agent receives only relevant context—a context engineering design problem