The short answer
An AI recommendation tracker records each prompt-engine answer as a dated observation, then separates mention presence, recommendation strength, position, reasoning, citations, competitors, and accuracy.
Business outcome
You can show whether the brand is entering more shortlists, being recommended for the right reasons, and gaining evidence-backed visibility over time.
The process
Build it in five passes
Define recommendation levels
Use a small fixed rubric: absent, mentioned incidentally, included in a consideration set, positively recommended, or top recommendation. Write examples for each level so two reviewers do not score the same wording differently.
Separate observation from interpretation
Store the answer text, citation URL, and list order as observed fields. Put reviewer judgments such as recommendation level, claim accuracy, and buyer fit in separate columns. This makes disagreements auditable.
Track the reason, not only the rank
Code the attribute attached to the recommendation, such as price, ease, local trust, technical depth, or enterprise readiness. A higher position for the wrong buyer or an outdated product claim may be less valuable than a lower but accurate recommendation.
Roll up by decision stage
Calculate coverage and recommendation share by discovery, comparison, objection, and purchase. Also show the domains most often cited and the competitor most often placed ahead of you. This identifies whether the next move is content, entity correction, access, or external proof.
Annotate every intervention
Log important page changes, research releases, PR placements, profile updates, crawler-policy changes, and known engine updates. Do not credit the nearest change automatically. Treat the annotation as a hypothesis to investigate.
Before it ships
Quality checklist
- The recommendation rubric has written examples for every level.
- Raw observations and reviewer judgments live in different columns.
- Each recommendation includes the reason or attribute used in the answer.
- Citations are saved as full URLs, not only domain names.
- Results can be filtered by engine, buyer stage, and snapshot date.
- Site and distribution changes are annotated without claiming causation.
Copyable artifact
Recommendation tracking schema
Use these fields in a sheet or database. One row equals one prompt-engine observation.
observation_id: [snapshot]-[prompt]-[engine] snapshot_date: YYYY-MM-DD prompt_id: stable ID intent_stage: discovery | comparison | objection | purchase engine_and_surface: exact product used brand_status: absent | incidental | considered | recommended | top_pick list_position: number or NA recommendation_reason: exact attribute or short paraphrase buyer_fit: strong | partial | poor claim_accuracy: accurate | incomplete | wrong | unverifiable cited_urls: full URLs separated by line breaks competitors_ahead: names in answer order raw_answer_reference: approved archive location intervention_note: change ID or none reviewer: initials
Validation
How you know it is ready
- 01Two people independently score a sample and resolve rubric ambiguity.
- 02The monthly summary reconciles exactly to the observation rows.
- 03Every trend can be filtered back to the prompts and answers behind it.
Do not overclaim
List position is not a universal AI ranking. Many answers use prose, reorder options, or tailor recommendations to context. Treat position as one coded feature, not the entire outcome.
Questions
What teams usually ask
Should a cited brand count as recommended?
Not automatically. A source citation, a brand mention, and a positive recommendation are different observations and should be tracked separately.
How often should the tracker be updated?
Monthly is usually enough for a focused program unless a launch or crisis requires closer observation. Use the same core prompts and settings for every matched snapshot.
Can this become one score?
It can, but always expose the components. A score that hides accuracy, prompt coverage, or citation context can reward the wrong outcome.
Sources reviewed