Which LLMs should I optimize for?

Optimize for major LLMs including GPT-4 (ChatGPT), Claude (Anthropic), Gemini (Google), and Llama (Meta). Focus on universal best practices—quality, clarity, authority—that work across LLMs rather than platform-specific tactics.

Do LLMs have access to current information?

It varies. Some LLMs have training cutoff dates and lack recent information. Others (like Perplexity, ChatGPT with browsing) access real-time information. Create evergreen authoritative content and keep it updated for maximum LLM visibility.

Can LLMs cite sources accurately?

LLMs can cite sources but may sometimes generate inaccurate citations or attribute information incorrectly. Clear source attribution in your content helps LLMs cite accurately. Monitor for citation accuracy.

How do I know if an LLM is citing my content?

Test by querying LLMs with relevant prompts and observing responses. Some LLMs explicitly cite sources; others synthesize without attribution. Use AI visibility tools to systematically track citations across platforms.

Large Language Models (LLMs): Definition & Examples | AEO/GEO Glossary - Reaudit

Definition

Large Language Models (LLMs) are AI systems trained on massive amounts of text data that can understand and generate human-like language. LLMs power popular AI assistants like ChatGPT, Claude, and Gemini, and are the core technology behind the AI search revolution.

LLMs work by learning patterns in language from their training data, enabling them to predict and generate relevant text responses. They can answer questions, summarize information, write content, and engage in conversation. Their capabilities have transformed how users seek and consume information.

For GEO, understanding LLMs is essential because they are the systems you're optimizing for. LLMs have specific characteristics: they synthesize information rather than just retrieve it, they have training data cutoffs (though some access real-time information), they evaluate source credibility, and they can make mistakes or "hallucinate" information.

Optimizing for LLMs means creating content that LLMs can accurately understand and cite. This requires clarity, accuracy, authority signals, and structure that helps LLMs identify and extract relevant information.

Key Factors

1

Content quality

2

Information accuracy

3

Authority signals

4

Clear structure

5

Source attribution

Real-World Examples

1
A marketing team studying LLM behavior to understand how their content might be cited
2
A content strategist creating LLM-friendly content structures based on how models process information
3
A brand monitoring multiple LLMs to understand differences in how they represent the brand

Frequently Asked Questions about Large Language Models (LLMs)

Learn more about this concept and how it applies to AI search optimization.

Large Language Models (LLMs)

Definition

Key Factors

Real-World Examples

Frequently Asked Questions about Large Language Models (LLMs)

Also Known As

Related Terms

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