What it means
A static pretrained LLM is a model whose knowledge is frozen at training time. It answers from patterns learned during pretraining, with no live retrieval, so it can't see anything published after its cutoff and it can invent facts that sound convincing. Kevin's guidance is to treat its output like anything else you read online: Verify before you trust it.
Why it matters
A static pretrained model can confidently state wrong or outdated facts about your brand, category, or pricing. Hallucination is the biggest risk in using generative AI, and the rates are high enough to matter: A Stanford study found 69% to 88% for legal queries, with broad estimates of 15-20% for older models like GPT-3 and around 3% for GPT-4 class models. A model with frozen knowledge has no way to correct itself between training runs.
Say a mid-market B2B SaaS company launches a new pricing tier. A static pretrained model trained before the launch keeps recommending the old plan and misstates the price to buyers who ask, and it stays wrong until the model is retrained or given live retrieval.
How to use this knowledge
Assume a static model's answers about you can be stale or invented, and check what the major models say about your brand. Favor AI surfaces that use live retrieval, where you can influence the pages that get pulled in, since a purely static model can't see your latest content. Verify model output before acting on it, especially for high-stakes or fast-moving facts. Keep your most authoritative, widely-referenced content accurate so the next training run learns the right version of you.
Growth Memo guidance
The key point is to not trust LLM output blindly, just like we shouldn't trust everything we read on the internet. — The state of generative AI for SEO (PT.2)
Hallucination — the convincing but false output a static model produces when it fills gaps from memory.
Knowledge cutoff — the training date past which a static model knows nothing.
Retrieval-augmented generation (RAG) — feeding live sources to a model so it isn't limited to frozen knowledge.
Grounding — attaching answers to retrieved sources, which lowers reliance on pretrained memory.