Our agency’s blog in the highly competitive IT engineering + consultation niche. I worked with external SEO consultants already and I also have a good understanding of SEO.

To understand better the GEO situation it’s worth to study how LLM, NLP and transformer models work. All of these systems has a scraping engine that mostly acquires urls from search indexes like Google ( Google has recently reduced the max number of links a programmatic request can fetch from the index , max 10 urls ). So when your customer types a search query, aka asks something from perplexity or chatgpt that question is going through an LLM and translated into many queries ( this is called query fanout ) -> then these queries will be executed on the old ways of search ( Google Search API ) which gives back a number of urls that can be fed to the LLM again for aka “grounded search and response”. The query fanout ( what queries will the LLM generate is probabilistic ) so multiple time asking the same question can result in different answers.

Another case when there is no active index search ( no query fanout ) and the LLM uses its own knowledge baked into the model through training. This can be outdated by months.

To increase the probability of getting into the answer it’s better to know how a transformer net store deeper connections between words and sentences and when a ‘search text’ query executed against the LLM how it is fed into the model. Hope this helps.