Large Language Model (LLM): A Plain Guide

A large language model (LLM) is an AI system trained on huge amounts of text to predict and generate language. It is the engine behind tools like ChatGPT, Claude, Gemini and Perplexity, and behind the AI-written answers that now appear in search results.

For marketers, LLMs matter because they increasingly sit between your brand and your customer. When someone asks an assistant for "the best payroll software for a 20-person company," an LLM decides how to answer and which sources to mention. Understanding how these models work, at least at a high level, helps you make better decisions about content and visibility.

What is a large language model?

Google's Machine Learning Crash Course defines a language model as one that estimates how likely a token, or sequence of tokens, is to appear in a longer sequence. A token is a small unit of text. Most modern models use subwords, so a token might be a whole word, part of a word or a single character. Google notes that in English, one token is roughly four characters.

A large language model is simply a language model with an enormous number of parameters, the internal settings it adjusts during training. According to Google's course material on LLMs, models built on the Transformer architecture can have hundreds of billions to trillions of parameters, and larger models have generally outperformed smaller ones.

In everyday terms, an LLM works like a very advanced autocomplete. You give it some text, such as a question, and it generates the most probable continuation, one token at a time. That continuation can be a word, a paragraph or a full article.

Why large language models matter for search and marketing

LLMs have changed where people get answers. Instead of scanning ten blue links, many people now read a synthesized response in an AI assistant or in Google's AI Overviews. If your brand isn't part of that answer, you may never get the click.

That shift is the reason generative engine optimization (GEO) exists. The original GEO research paper from 2023 framed generative engines as a new kind of search interface and reported that certain content changes could boost visibility in their responses by up to 40% in the authors' tests. Results like that come from controlled experiments, not guarantees, but they show that content choices can affect what LLM-powered systems surface.

LLMs also affect your own work. Teams use them to draft content, summarize research and analyze data. Knowing their strengths and weaknesses helps you use them responsibly.

How large language models work

You don't need a computer science degree to understand the basics. Four ideas cover most of what marketers need to know.

Transformers and self-attention

Most LLMs are built on the Transformer architecture. Its key feature is self-attention, which lets the model weigh how much every other token in the input affects the meaning of each token. That is how a model works out which noun "it" refers to in a sentence. Google's course explains that, unlike older recurrent networks that processed text word by word, LLMs can evaluate the whole context at once.

Pre-training

Models first learn from massive amounts of filtered text. During this stage, the model repeatedly predicts missing tokens, checks how wrong it was and adjusts its parameters. Over many rounds, it picks up grammar, facts, styles and patterns. This knowledge is frozen at a point in time, often called the training cutoff.

Instruction tuning and alignment

A raw pre-trained model is good at continuing text but not necessarily at following directions. An extra step, which Google calls instruction tuning, teaches the model to respond helpfully to requests. Companies also add safety and behavior training on top.

Retrieval and grounding

Because training data goes stale, many AI products connect the model to live information. They search the web or a document store, feed the results into the prompt and ask the model to answer using them. This is called retrieval-augmented generation (RAG), or grounding. Google's guide to generative AI features in Search says AI Overviews and AI Mode work this way, retrieving relevant pages from the Search index before generating a response.

Large language models in practice

Here is a generic example of an LLM answering a buying question. A user asks an AI assistant, "What should I look for in a commercial roofing contractor in Phoenix?"

The assistant decides whether it needs fresh information. For a local, commercial question, it likely runs one or more web searches.
It retrieves pages: perhaps a few contractor sites, a licensing board page and a buyer's guide from a trade publication.
The LLM reads those pages, combines them with what it learned in training and writes an answer covering licensing, warranties, heat-resistant materials and references.
It cites the pages it relied on, and may name specific contractors.

A contractor with a clear, specific page about commercial roofing in Phoenix, consistent business details across the web and helpful third-party coverage has a better chance of being one of those retrieved pages. Nothing guarantees inclusion, but clarity and credibility help.

Best practices for showing up in LLM answers

Get the SEO basics right. Google says its AI features are built on its core ranking systems, and pages must be indexed and eligible for a snippet. Crawlability and indexing still come first.
Publish original, specific content. Google's AI optimization guide recommends unique, non-commodity content based on first-hand experience. Generic summaries give a model little reason to choose you.
Structure pages clearly. Use descriptive headings and short, direct paragraphs so key facts are easy to find and quote.
Be consistent about who you are. Keep your name, services, locations and claims the same across your site, profiles and directories, so models can connect them to one entity.
Earn credible mentions. Coverage on reputable third-party sites gives retrieval systems more trustworthy pages that reference you.
Test real prompts. Ask the assistants your customers use the questions they would ask, and record what comes back over time.

Common mistakes and misconceptions

"LLMs know everything." They don't. Their built-in knowledge stops at the training cutoff, and they can produce confident errors. Google lists hallucination, or incorrect output, as a known problem with LLMs.

"LLMs look facts up in a database." A model on its own predicts text from patterns. It only "looks things up" when the product around it adds retrieval.

"Every answer is the same." LLM output varies. The same prompt can produce different wording, different lists and different brands from one run to the next.

"You need special AI files or rewritten content." Google's guide says you don't need llms.txt, content "chunking" or AI-specific rewrites for its generative features.

LLM vs. AI chatbot

People often use these terms interchangeably, but they are different layers. The LLM is the underlying model. A chatbot or assistant, such as ChatGPT or Perplexity, is a product built around one or more models, adding features like web search, memory, citations and safety controls. Perplexity, for example, describes itself as searching the web in real time and using LLMs to summarize results with numbered citations.

How Butter approaches large language models

At Butter, we look at LLMs from two sides: as the systems our clients want to be visible in, and as tools we use carefully in our own work.

On the visibility side, our GEO plan includes monthly AI prompt testing, where we run the questions a client's customers actually ask and track how assistants respond. We pair that with indexing strategy, schema markup guidance and knowledge graph submissions so the brand's facts are easy for retrieval systems to find and interpret, plus one AI-crawlable content citation and backlink each month.

On the production side, our GEO+SEO plan publishes eight AI-generated articles a month. We use LLMs to speed up drafting, not to replace judgment, and we know from years in SaaS growth and agency SEO that thin, generic content rarely holds up. We never promise placement in AI answers, because no one can. You can see the details on our pricing page.

Frequently asked questions

What is the difference between an LLM and AI?

AI is the broad field of building systems that perform tasks that usually need human intelligence. An LLM is one specific type of AI model, trained on text to understand and generate language. ChatGPT, Claude and Gemini are products powered by LLMs.

Do LLMs search the internet?

Not by themselves. A model's built-in knowledge comes from training data with a cutoff date. Many AI products add a search step that retrieves current web pages and passes them to the model, which is why some assistants can cite recent sources.

Can I control what an LLM says about my brand?

You can't control it directly, but you can influence the inputs. Clear, accurate pages on your site, consistent business information and credible third-party coverage give AI systems better material to draw from when they retrieve sources.

Why do LLMs sometimes get facts wrong?

LLMs generate the most probable text based on patterns, not verified facts, so they can produce confident mistakes called hallucinations. Grounding answers in retrieved sources reduces this risk, but it doesn't eliminate it.

Sources and further reading

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