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Large Language Model Optimization (LLMO)

Large Language Model Optimisation (LLMO) refers to two different phenomena: the first is the optimisation of the Large Language Models themselves and the second is the optimisation of content for LLMs.

1. Optimisation of LLMs: Methods and strategies for improving the efficiency, performance and practicability of large language models (LLMs) when used in productive environments.  
This meaning is not explained further in this glossary entry. More information here about: A Comprehensive Guide to Optimising Large Language Models in Production.

2. Optimisation of content for LLMs - the aim here is the visibility of websites or content in LLM responses. This involves designing content on websites in such a way that it is preferentially mentioned or cited in responses by large language models such as ChatGPT, Google Gemini or Perplexity AI. It is based on search engine optimisation (SEO) in terms of its objectives and many measures.

Related Terms:

Basically, the term LLMO is not yet clearly defined and also not clearly differentiated from neighbouring terms, which also describe the optimisation of content for AI:

 

Strategies and methods in the context of Large Language Optimisation

LLMO (Large Language Model Optimisation) in the sense of content optimisation aims to design content in such a way that it is preferentially recognised, understood and used in responses by generative AI systems such as ChatGPT, Google Gemini (Google Overview), Mircrosoft Copilot or Perplexity AI.

Strategies:

  • Objective: The main objective is to increase visibility in AI-generated search and response systems. In this context, visibility primarily means that the optimised website appears as a reference in the text or as a supplementary source citation.

  • Platform selection: Optimisation should be based on the most important platforms: In addition to Google AI Overview, which every user encounters as part of a conventional Google search, ChatGPT and by far Microsoft Copilot and Perplexity are the most widespread.

  • Content strategy: Focus on semantic depth, thematic authority and complete context coverage. Content should contain relevant terms, definitions and examples in order to be considered a comprehensive source.

Necessary measures:

  • Content design:
    • Clear, concise sentences (5-25 words), one idea per paragraph
    • Headings and lists for structuring, answer-first principle (first summary, then details)
    • Use semantic HTML structures and schema markup (e.g. FAQPage, HowTo, Article) to increase machine readability.
    • Integration of Q&A sections and definitions to answer typical user questions directly
  • Specific optimisation:
    • Early definition of key terms and topics
    • Use of thematically related terms and consistent terminology
    • Integration of current, verifiable facts and sources
 
Similarities between LLMO and classic SEO
Aspect LLMO classic SEO
User Intent Aims to capture the intent behind complex questions and prompts to provide precise and helpful answers. Understands what users are looking for in search engines to provide appropriate content.
Keyword Relevance Involves integrating relevant phrases and concepts that an LLM might recognize and respond to, enhancing the likelihood of your content being highlighted in AI-driven searches. Relies on the strategic placement of keywords and phrases into content that potential customers are searching for.
Content Quality Prioritize coherent, contextually rich, and informative content, meaning brands must prioritize depth and accuracy in their messaging. High-quality content is central to rankings - informative content, reader guidance and credibility are crucial.
Structured Data Benefits from clear entity markup (e.g. with schema markup) and helps the model to interpret content correctly and avoid errors. Utilizes structured data to help search engines understand content.

Source: https://dune7.co/is-large-language-model-optimization-llmo-the-new-seo

 
Key differences between LLMO and classic SEO
Aspect
LLMO
classic SEO
Objective Be listed as reference in answers. Rank on page 1 on Google.
Importance of backlinks Backlinks are less relevant for LLMs. Instead, these models prioritize the relevance and depth of information. The focus is on providing the most contextually appropriate and informative answers based on the model’s training data. Backlinks and domain authority still play a key role in ranking pages on Google. Earning high-quality backlinks from authoritative sites is critical for improving visibility.
Authority and trust Authority is also generated by the mere mention in other sources or in reviews. Domain authority is primarily based on backlinks, brand awareness and technical SEO.
What are the basics Linguistic patterns are stored Indexing: URLs, documents, link structure, crawl depth
Source: Warum KI-Sprachmodelle (LLMs) keine URLs speichern – und was das für SEO bedeutet

 
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