The answer depends on the AI model.
Some AI models utilize live web search (also known as search augmentation or RAG), while others rely entirely on static training data from months prior.
While in the future, all AI models will include live search, for now, customers are still using some models that reference their own foundational data.
Why does this matter?
Gumshoe helps you test how both types of models see your brand:
- If you're focused on fast impact, pay attention to your Visibility Audit (formerly known as a report) results from models with citations.
- If you're considering long-term influence and influencing "static" models, you'll need a sustained content presence to appear when they're next retrained.
In short, Gumshoe provides a comprehensive view of what all the models are saying, no matter how they collect their data.
Which models use live web data?
These models issue real-time search queries and pull current information from the web. You'll know they're doing this if your Visibility Audit includes citations. Citations are direct clues that the model searched the internet for its answer.
- These models reflect recent content updates
- They're more responsive to changes you make on your website
- They're useful when you want to test visibility right now
To see exactly which models pull from live web data, check the Live Web Search field in the tooltip next to each model on your project's Settings page.
Which models use static training data?
Other models don't use the internet when answering. They rely on the data they were trained on, which usually has a cutoff date that varies by model. There is, however, likely some training that the models undergo through their interactions with customers. AI models do not share when their "static" models will be updated; it could happen at any time. Quickly optimizing your brand for AI visibility and maintaining it regularly will ensure readiness for any future updates.
- These models don't cite sources
- They won't reflect recent content changes until the next big update for the model
- Many users are interacting with these static models