Since the introduction of generative AI in the form of ChatGPT, BARD or SGE, Large Language Models (LLMs) have conquered the world and also found their way into search engines. SEOs worldwide are discussing the possibility of proactively influencing AI output via Large Language Model Optimization (LLMO), Generative Engine Optimization (GEO) or Generative AI Optimization (GAIO) .
Some SEOs even talk about the future of SEO . This article will address the romania cell phone number list future importance of LLM optimization (LLMO) in the context of SEO and also critically include the view from the data science perspective.
Table of contents [Hide]
1 Was ist LLM-Optimization (LLMO) / Generative AI Optimization (GAIO) / Generative Engine Optimization (GEO)?
2 How do such recommendations come about?
3 How do Large Language Models (LLMs) work?
4 Can the results of generative AI be proactively influenced?
5 How could the training data for the LLMs be selected?
6 Conclusion
Was ist LLM-Optimization (LLMO) / Generative AI Optimization (GAIO) / Generative Engine Optimization (GEO)?
The aim of GAIO, GEO and LLMO is to enable companies to position their brands and products prominently in the outputs of the leading Large Language Models (LLM) such as GPT and Google Bard, as these models can influence many purchasing decisions in the future.
For example, if you search in Bing Chat for the best running shoe for a runner weighing 96 kilograms who runs 20 kilometers per week, shoe models from the brands Brooks, Saucony, Hoka and New Balance are suggested.
If you ask Bing Chat about cars that are safe, family-friendly and big enough for shopping and traveling, car models from the brands KIA, Toyota, Hyundai and Chevrolet are suggested.
The approach of possible procedures such as LLM optimization aims to favor certain brands and products in corresponding transaction-oriented questions.
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