AI search vs. LLM - what is the difference?

You'll see both terms everywhere — and the difference matters for your brand visibility. Here's the short version:

An LLM answers from memory. AI search answers from memory plus a live web search — with citations.

What is an LLM?

A Large Language Model (like GPT or Gemini, the models themselves) generates answers from what it learned during training. Think of it as answering from memory:

Knowledge has a cutoff date — it doesn't know about last week
No sources or links — it can't show where a claim comes from
Brand knowledge is baked in slowly, through training

What is AI search?

AI search combines an LLM with live web retrieval (a technique called RAG — Retrieval-Augmented Generation). When you ask ChatGPT something and see "Searching the web…", that's AI search in action:

Pulls in fresh, current web content at the moment you ask
Cites sources — links to the pages it drew from
Reflects what's on the web today — including your content

Side by side

	LLM (alone)	AI search (LLM + web)
Knowledge	Training data, with cutoff	Live web results, current
Sources shown	None	Citation links
Can your new content appear?	Only after retraining	Yes — as soon as it's crawlable
Example	ChatGPT without browsing	ChatGPT with browsing, Google AI Overviews, Perplexity

Why this matters for your brand?

AI search is where visibility is winnable today. You can't quickly change what a model memorized in training, but you can influence what AI search retrieves and cites right now, by publishing crawlable, citable content. That's the entire premise of GEO (Generative Engine Optimization), and it's the layer OtterlyAI monitors: which brands appear, and which websites get cited, in real AI search answers.