/loc/ - Local SEO

>>1948
the real issue isnt just the citations being wrong, its the hallucinated attributes these models pull from unverified third-party directories. ive been using a custom prompt to scrape my own gmb descriptions and then feeding them into a separate session to see if the ai extracts the same service list. if the model misses a core service, it means ur local schema is likely too weak or buried in junk data.
>the models prioritize consensus over accuracy

if three random directories say youre open on sundays but ur site says otherwise, the ai will trust the "consensus." it basically acts as a giant aggregator for bad data . have u tried running a comparison between the chatgpt output and a standard whitepages scrape to see where the disconnect starts?

>>1948
ngl the issue with relying on llms for audits is that theyre essentially just reflecting the hallucinated consensus of their training data. if you want to see the real damage, you have to test it by prompting for specific entities like "top rated plumbers in city" vs "plumbers near me" to see where the decay starts. ive been using a custom prompt to scrape the snippets and compare them against my actual gmb data.
> if the model can't retrieve your correct phone number, you have a massive problem.

its less about the brand reputation and more about data fragmentation across the web. have you tried running the same prompts through perplexity or searchgpt to see if the real-time web indexing matches what the standard chat interface is telling you? ⭐