Large Language Model (LLM)

What a large language model is

A large language model, or LLM, is trained by learning statistical patterns across enormous text datasets — billions of words drawn from web pages, books, articles, and other sources. That training lets it generate coherent, contextually appropriate text, answer questions, summarise information, and produce recommendations that read as though a knowledgeable person wrote them, even though nothing about the underlying process resembles human reasoning directly.

Two separate sources of what an LLM "knows"

An LLM's knowledge about a business comes from one of two places, and the distinction matters a lot for anyone trying to influence what it says. Pre-training data is whatever the model learned about entities mentioned in web content before its training cutoff — this is fixed once training finishes and doesn't update on its own. Browsing, where the model has been given that capability, lets it retrieve live web sources to answer a current query instead of relying purely on memory.

A model without browsing enabled is answering entirely from whatever it absorbed during training, which might be a year or more out of date by the time a user asks a question. A model with browsing enabled is combining that older training knowledge with whatever it retrieves live — which is closer to how grounding works, and reduces (without eliminating) the risk of hallucination.

Why LLM responses function like recommendations

Being named favourably in an LLM's response to "best dermatologist in Bengaluru" functions, for practical purposes, similarly to a word-of-mouth recommendation from a friend — the person asking trusts the answer enough to act on it without independently verifying it, the same way they'd trust a friend's suggestion. This is the mechanism that makes LLM visibility a genuine business concern rather than an abstract technical curiosity: a business that isn't named simply doesn't get considered, the same way it wouldn't get considered if nobody ever mentioned it to a friend asking for a recommendation.

Why understanding this split matters for optimisation

Recognising that LLMs work through training data plus optional browsing, rather than through any kind of live database lookup, explains why effective optimisation needs two different things working together. Content structure that survives browsing-based extraction cleanly — clear, factual, well-marked-up pages — matters for the live-retrieval half. Entity consistency sustained over time matters separately for the training-data half, since whatever gets absorbed into a future training run depends on what's consistently said about the business across the web during the period that data gets collected.

A business that fixes its website today gets an immediate benefit for browsing-capable systems, but improving what a model "remembers" from training takes longer, because that only updates when a new training run happens and pulls in fresher web data.

LLMs used in India

GPT-4 and its successors from OpenAI, Gemini from Google, and Claude from Anthropic are the primary large language models in active use in India as of 2026. All are accessible and capable in English; Hindi and other regional language capability varies meaningfully between them and continues to improve, though it still generally lags English-language performance across all three.

What this means for a local business

There's no single dashboard or submission form for influencing an LLM's opinion of a business directly. The two real levers are the same ones covered under GEO — building a consistent, factual, well-structured presence that a browsing-capable model can retrieve accurately right now, and sustaining that same consistency long enough that it eventually gets absorbed into future training data too.

Related terms: RAG → · GEO → · LLM citation → · AI Overview → · Grounding → · Hallucination →

Example: When a user asks ChatGPT about physiotherapy clinics in Pune, ChatGPT draws on both its training data — whatever it absorbed about Pune physiotherapy clinics before its cutoff — and, where browsing is enabled, live retrieval from Practo pages and current search results, combining both into the recommendation it actually gives.

Generative Engine Optimisation Service →