Definition
AI Brand Recall measures how easily models bring up your brand for relevant prompts. It’s influenced by evidence quality, retrieval configuration, and model familiarity with your brand.
Why this matters
Low recall means you’re forgotten in critical journeys. Improving recall increases top-of-mind presence and share of recommendations.
Common types
Intent-Level Recall
Recall for specific topics or jobs-to-be-done.
Model-Level Recall
Differences in recall across ChatGPT, Claude, Gemini, etc.
Geo/Persona Recall
Recall variance by market or audience.
Evidence-Driven Recall
Impact of evidence quality and freshness on recall.
Real-world examples
1Recall lift
After adding concise briefs, brand shows up reliably in core intents.
2Model gap
Good recall on ChatGPT, weak on Gemini; model-specific updates fix it.
3Persona alignment
Role-specific examples improve recall for executive queries.
How to use this in VisibleLLM
Use VisibleLLM to measure recall by intent/model/geo, upgrade evidence, and iterate prompts; verify gains with evals.
Start for freeBest practices
- Ensure concise, authoritative evidence for top intents.
- Localize examples for key markets and personas.
- Tune retrieval and prompts per model where recall is weak.
- Monitor recall alongside answer share and substitution risk.
- Re-evaluate after each content or prompt update.
Frequently asked questions
How is recall different from answer share?
Recall checks if you’re retrieved/mentioned; answer share tracks inclusion and recommendation strength.
Why model gaps?
Each model ingests and ranks differently; tailor evidence and prompts per model.
How often to measure?
Weekly for fast-changing topics; monthly for stable ones.