A Large Language Model (LLM) is the AI technology that powers modern answer engines like ChatGPT, Claude, Perplexity, and Gemini. LLMs are trained on enormous datasets of text (web pages, books, code, and other content) and learn to predict what text should follow a given input - enabling them to generate coherent, contextually relevant prose responses to questions.
The major LLMs and what they power
| Model | Company | Powers |
|---|---|---|
| GPT-4o, GPT-4o-mini | OpenAI | ChatGPT |
| Claude 3.5 Sonnet, Haiku | Anthropic | Claude.ai |
| Sonar | Perplexity AI | Perplexity |
| Gemini | Google AI Overviews, Gemini Assistant | |
| Llama 3 | Meta | Open-source, powers many third-party apps |
Each model has different training data, different strengths, and different patterns in how it recalls and describes brands. This is why AI visibility monitoring must cover multiple models - your brand may be well-represented in one LLM’s training data but absent or misrepresented in another’s.
How LLMs “know” about your brand
LLMs do not have explicit knowledge lookup tables. Their “knowledge” is encoded in billions of numerical parameters during training. If your brand appeared frequently in their training data - in reviews, articles, documentation, directories - those patterns are encoded in the model’s weights and influence what it says when asked about your category.
This is why:
- Brands with broad web presence tend to appear in more AI answers
- Newer brands (launched after the model’s training cutoff) may not appear at all without active retrieval
- Brands that are described consistently across many sources are more reliably represented than brands with fragmented or inconsistent descriptions
The training cutoff problem
Every LLM has a training cutoff date - a point after which new web content was not included in its training. Models trained in 2024 may not know about products launched in 2025, recent price changes, or new features.
This cutoff creates a challenge: even if you publish excellent new content today, models with static training will not incorporate it until their next training cycle. Models with web retrieval capabilities (like Perplexity and some ChatGPT versions) partially address this by fetching current content at query time.
For AEO purposes, the practical implication is to ensure your brand has had strong presence across the web for a sustained period, not just recently. Long-standing directory listings, persistent review profiles, and evergreen content on authoritative sites are more valuable for LLM training representation than recently published blog posts.
Why different LLMs behave differently about your brand
Even for the same prompt, different LLMs may give very different answers about your category. Some factors:
- Training data composition: different providers selected different training corpora
- Instruction tuning: different fine-tuning approaches make models more or less willing to make product recommendations
- Retrieval augmentation: models with active web retrieval reflect current web content; purely parametric models reflect their training snapshot
- Model size and capability: larger models often have better entity recall
This variability is exactly why tracking across multiple LLMs (as AskRank does) gives a more complete picture of your AI visibility than monitoring any single model.