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URL: https://geekytech.co.uk/llm-training-data-vs-web-search
This article compares Large Language Models (LLMs) and traditional search engines, explaining how LLMs function and why they may not always use the most current content. It introduces Generative Engine Optimisation (GEO) as a strategy to improve content visibility in AI-powered answers that utilize web searches.
LLMs, Search Engines, Generative Engine Optimisation, GEO, AI, content optimisation, web search functionality
Q: What is a Large Language Model (LLM)?
A large language model, or LLM, is a type of generative AI that processes, understands, and creates natural-sounding language.
Q: How do LLMs work?
LLMs are trained on large amounts of text data to learn language patterns and structures, enabling them to predict the next word in a sentence and generate relevant responses.
Q: Why aren't LLMs using my content?
LLMs have training cutoff dates and do not continuously learn from new data. If content was published after the cutoff date, the LLM won't know about it unless it can access the internet via a web search.
Q: What is Generative Engine Optimisation (GEO)?
Generative Engine Optimisation (GEO) is a strategy for optimising content to improve its visibility in AI-powered answers that involve external information retrieval through web searches.
Q: How do LLMs find content on search engines?
When a user query requires up-to-date or complex information, the LLM uses a dedicated web search tool or API to retrieve data, processes it, and then combines it with its existing knowledge to generate a comprehensive answer.
Q: How can I improve my content's visibility in AI answers?
To improve visibility, continue with SEO best practices, create well-written and comprehensive content, use conversational language and long-tail keywords, employ logical headers, interlink related pages, and maintain a strong online presence across various platforms.