LLM perception drift measurement protocol
This protocol defines a minimal way to measure LLM perception drift without reducing it to an AI visibility score. It applies when an entity, brand, person, offer, or doctrine must be observed in the answers of several generative systems.
Phase 1: establish the canon
The first step is to identify admissible sources:
- canonical entity definition
- role or positioning page
- evidence pages
- service or offer pages
- related definitions
- explicit exclusions and limits
- relations between entities, brands, products, and doctrines
Without this step, the audit only measures an impression. The canon makes the gap measurable.
Phase 2: create a baseline
The baseline documents the initial state of AI perception. It should record:
- models or engines queried
- exact prompts
- observation date
- visible parameters
- language
- cited or used sources
- generated summary
- assigned categories
- competitors or neighbors mentioned
- significant absences
The baseline is not a neutral snapshot. It is a governed comparison point.
Phase 3: qualify the canon-output gap
Each output is compared with the canon across five axes:
- identity: who or what is being reconstructed?
- category: which market or frame is the entity placed in?
- perimeter: which activities, limits, or exclusions are preserved?
- evidence: which sources support the answer?
- recommendability: is the entity proposed, ignored, or displaced?
An isolated gap should be recorded. A repeated gap should be qualified. A gap that stabilizes becomes a drift signal.
Phase 4: observe variation
Drift should be observed along at least one axis:
- variation over time
- variation across models
- variation across languages
- variation across prompts
- variation across intents
- variation across cited sources
- variation between browsing and non-browsing answers
This step separates a one-off error from a trajectory.
Phase 5: decide correction
Correction should not be automatic. It may target:
- canonical content
- internal linking
- disambiguation
- external evidence
- perimeter clarification
- a contaminating source
- a new definitional page
- entity graph reinforcement
The protocol does not promise immediate correction by models. It measures the capacity for resorption after the corpus is changed.
Expected output
A useful measurement produces at minimum:
- a baseline
- a gap map
- a drift classification
- a correction priority
- a cause hypothesis
- a re-observation plan
- an interpretive risk indication
The result is not only a visibility dashboard. It is a governed reading of generated representation.