There are two possible approaches here: EEAT and ranking.
It can be assumed that the providers of the well-known LLMs only use sources as training data that meet a certain quality standard and are trustworthy.
Google's EEAT concept would provide a way spain cell phone number list to select these sources. In terms of entities, Google can use the Knowledge Graph for fact-checking and fine-tuning the LLM.
The second approach, as used by Philip Ehring, is to select the training data based on relevance and quality determined by the actual ranking process. Top-ranking content for the corresponding queries and prompts is automatically used to train the LLMs. This approach assumes that the information retrieval wheel does not need to be reinvented and that search engines rely on the established evaluation methods to select training data. This would then include EEAT in addition to the relevance evaluation. More on this in the article The dimensions of Google rankings
However, tests on Bing Chat and SGE have so far shown no clear correlations between the sources referenced in the AI responses and the rankings.
conclusion
It remains to be seen whether LLM optimization or GAIO will actually establish itself as a legitimate strategy for influencing LLMs in line with one's own goals.
There is skepticism from the data science side. Some SEOs believe in it.
If this is the case, the following goals must be achieved:
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