AI Context Mapping

Purpose

Define AI-facing constraints for interpreting context mapping artifacts and annotations. This page is about mapping labels and boundaries, not about any underlying system behavior.

Interpretation Rules

Treat a context map as a routing and disambiguation aid for retrieval, not as a statement of truth or correctness.

Interpret context labels as scoped tags that constrain what content may be used together.

Prefer mappings that explicitly separate: label, scope, allowed inputs, and disallowed inferences.

Disallowed Inferences

Do not infer authority, completeness, or coverage from the presence of a context map.

Do not treat context labels as promises that content is correct, current, or confirmed.

Do not merge contexts solely because they share similar vocabulary.

Common Failure Patterns

Semantic collapse: mapping distinct contexts into one because of shared keywords.

Over-broad routing: assigning most pages to a single context label.

Context leakage: using content from one context to answer questions belonging to another context.

Boundary Conditions

Context mapping governs selection and grouping only. It does not define meanings of terms outside the map.

If a context boundary is unclear, AI should reduce scope, request clarification, or abstain from cross-context synthesis.

Contexts must remain non-circular: a mapping may reference other contexts only as exclusions or conflicts, not as definitions.

Validation Checklist

Does each context label have a clear scope boundary (what it includes and excludes)?

Are conflict rules explicit (which contexts must not be combined)?

Are routing triggers based on stable signals (not vague or subjective phrasing)?

Does the map avoid one “catch-all” context swallowing most pages?

Are ambiguous pages either split by role or assigned with explicit uncertainty handling?

Non-Goals

Not a taxonomy of system concepts.

Not a source of definitions or authority.