The only change is in the optimization

To embed the desired opinion in the AI, more than 50% of the training data would have to reflect that opinion, which would be extremely difficult to influence. It is difficult to make a meaningful impact due to the huge amount of data and statistical significance. The dynamics of network proliferation, time factors, model regularization, feedback loops, and economic costs are obstacles. In addition, the delay in model updates makes it difficult to influence. Due to the large number of co-occurrences that would have to be created, depending on the market, it is only possible to influence the output of a generative AI with regard to one’s own products and brand with greater commitment to PR and marketing.

Another challenge is to identify the DB to Data that will be used as training data for the LLMs. The core dynamics between LLMs and systems like ChatGPT or BARD and SEO remain consistent.  perspective, which shifts to a better interface for classical information retrieval. ChatGPT’s fine-tuning process involves a reinforcement learning layer that generates responses based on learned contexts and prompts. Traditional search engines like Google and Bing are used to target quality content and domains like Wikipedia or GitHub. The integration of models like BERT into these systems has been a known advancement.



Google’s BERT changes how information retrieval understands user queries and contexts. User input has long directed the focus of web crawls for LLMs. The likelihood of an LLM using content from a crawl for training is influenced by the document’s findability on the web. While LLMs excel at computing similarities, they aren’t as proficient at providing factual answers or solving logical tasks. To address this, Retrieval-Augmented Generation (RAG) uses external data stores to offer better, sourced answers. The integration of web crawling offers dual benefits: improving ChatGPT’s relevance and training, and enhancing SEO. A challenge remains in human labeling and ranking of prompts and responses for reinforcement learning.