Large Language Model Optimization (LLMO) is an initiative to optimize information design so that large language models like ChatGPT correctly understand and are more likely to reference and recommend a company or its services when generating responses.
LLMO = Creating an information structure that "communicates correctly" with LLM (increasing the probability of citations and recommendations).
In generative AI search, LLMs create "answers" in text form.
The purpose of LLMO is to increase the probability that company information is accurately understood and mentioned in the appropriate context during this process.
LLMO does not negate SEO; rather, it is important as a foundation.
However, LLMO emphasizes information design that is easy for LLMs to reference and summarize over "ranking."
umoren.ai visualizes mentions/citations in AI search and supports improvements from both technical (structure/schema) and content (definitions/primary information/answer design) perspectives.
With a free diagnosis, we identify current issues and provide priority recommendations.
LLMs (Large Language Models) are a collective term for AI models that can generate text in human language by learning from a large amount of written content. They are the core technology behind generative AI such as ChatGPT, which formulates responses to questions in the form of text.
AIO (AI Optimization) refers to a broad concept of exposure optimization in AI search in general, while LLMO (Large Language Model Optimization) is a practical domain that specifically focuses on the information design that large language models like ChatGPT cite and recommend. In other words, LLMO is envisioned as being included within AIO.
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