What Are Large Language Models (LLMs) and How Do They Work

If it feels like ChatGPT, Gemini, Claude, and Perplexity appeared out of nowhere and ended up everywhere, that’s because they sort of did. Customers are researching services with them, teams are drafting emails and summarizing documents with them, and Google uses one to generate those AI Overviews now appearing above every other search result.

Whether you’ve touched any of these tools yourself or not, you’re already operating in a world shaped by them.

You don’t need a technical background to understand what’s going on. If you run a local business, though, it’s worth knowing what large language models are, because they’re already changing how customers find you.

First, What Is a Large Language Model?

A large language model, LLM, is the AI engine powering tools like ChatGPT, Gemini, and Claude. You type a question, it reads what you wrote and generates an answer, but not by looking it up. It predicts what a useful response looks like based on everything it learned during training.

That training involved reading hundreds of billions of words from across the internet. The scale is what makes these tools as capable as they are, rather than just clever autocomplete.

We’ll go deeper on how they work further down. First, the part that matters most for your business.

How Large Language Models Actually Work

A large language model is trained by reading enormous amounts of text and learning to predict what comes next in a sentence. Do that billions of times, and something interesting emerges: the system gradually develops an understanding of language, facts, tone, and context and not in the same the way a person does. LLMs use a pattern recognition at a scale that’s hard to picture.

The result is a system that can write, summarize, translate, answer questions, and explain complex topics in plain language without anyone explicitly programming it to do any of those things.

The Model and the App Are Two Different Things

One thing worth clearing up: the LLM is the engine underneath. The app you log into every day, such as ChatGPT, is built on top of it.

ChatGPT is a product OpenAI built on top of their GPT language model. Google built their AI Overviews and Workspace features on top of Gemini. Anthropic built the Claude app on top of their Claude model.

The engine and the product you use every day are related, but they’re not the same thing. Think of it like the difference between an operating system and the apps installed on it: one makes the other possible, but they serve different purposes.

The Two Stages of Training

LLM learning happens in two stages.

Stage one: reading everything. The model is exposed to an enormous amount of text and learns to predict what comes next. What word follows this sentence, what idea follows this paragraph. It does this billions of times, gradually absorbing how language works, how facts connect, and how different types of content are structured. Nobody tells it what anything means. It figures out the patterns entirely on its own.

Stage two: human feedback. This is where trainers review the model’s responses and provide feedback on what’s helpful, accurate, and appropriate. This shapes the raw model into something useful for real conversations, rather than just a very sophisticated autocomplete.

How LLMs Answer a Question

When you type a question into one of these tools, it’s not looking up a stored answer from a database. It’s generating a response word- by-word, each time predicting the most likely next word given everything that came before it.

This is why:

• The same question can produce slightly different answers on different days and for different people.
• These tools can give you wrong information with total confidence. The model produces what sounds most probable, not necessarily what’s true or always factual. 
• Human review matters for anything factual and you must review LLM outputs. 

The LLM Tools You’ve Already Heard Of

Who Makes What

The landscape shifts quickly, but these are the main players you’ll encounter:

• ChatGPT (OpenAI): The tool that introduced most people to AI assistants when it launched in November 2022. Still the most widely used and recognizable name around.
• Gemini (Google): Google’s AI model, built into Search, Google Workspace, and Android. It’s what powers the AI Overviews at the top of Google’s search results.
• Claude (Anthropic): Known for handling long documents reliably and with a strong focus on safety. Built by a team of seven former OpenAI researchers who left in 2021 over disagreements about AI safety priorities, led by siblings Dario and Daniela Amodei.
• Llama (Meta): Meta’s model, with publicly available weights that developers can access and build on, which is why a lot of custom business tools are built using it as a foundation.
• Perplexity: Think of it as a search engine powered by AI. It answers questions with cited sources and sits somewhere between Google and a chatbot.

What Makes Each One Different

At a surface level, they all do the same thing: you type something in, you get a thoughtful answer back. The differences come down to what each company focused on when building them.

Gemini is deeply woven into Google’s products and works well with images and audio as well as text. Claude tends to handle very long documents without losing the thread. ChatGPT has the largest ecosystem, the most integrations, and the widest existing user base.

The landscape is still shifting fast. What’s true today about which tool does what best may look different six months from now.

What LLMs Can and Can’t Do

Where They Help

LLMs are good at language tasks that used to eat up hours: summarizing a long report, drafting a first version of an email or proposal, explaining something technical in simpler terms. For most businesses, the starting point is using ChatGPT or Claude to handle the writing and thinking tasks you’d otherwise spend an hour on.

The productivity gains are real. Just don’t skip the human review step, especially for anything factual.

Notable Limitations

Hallucinations: this is the term for when an LLM gives you a confident, fluent, completely wrong answer. It’s not being purposefully deceptive, it’s doing exactly what it was trained to do, which is to produce the most probable next word. Sometimes that process generates things that sound completely plausible but are factually incorrect.

As IBM explains it, the model generates information that is false or misleading while sounding plausible.

The knowledge cutoff: most LLMs were trained on information up to a specific date and have no knowledge of what happened after that. Some tools, like Claude and certain versions of ChatGPT and Gemini, can search the web in real time. Others are working from a fixed snapshot.

For anything time-sensitive, it’s worth knowing which category the tool you’re using falls into.

The Impact of LLMs On Your Business

When someone asks Google a question, the AI Overview answer that might appear at the top of the page was written by an LLM. When someone asks ChatGPT or Perplexity which plumber to call in their city, or which dentist is taking new patients nearby, an LLM is assembling that response from whatever information it has available about local businesses.

This is already happening. The businesses showing up in those answers aren’t there by accident.

The good news is that what LLMs draw on to recommend local businesses is largely the same stuff that’s always mattered in Local SEO:

• Your Google Business Profile: is it complete, accurate, and active?
• Your reviews: how many do you have, how recent, and what are people saying?
• Your website content: does it clearly explain what you do and where you do it?
• Your business information across the web: is your name, address, and phone number consistent everywhere?

These are the same things that have always driven Local SEO. The channel is new, but what makes you worth recommending hasn’t changed.

If those foundations are solid, you’re already in a better position than most. If they’re not, that gap is getting more expensive every month.

Where This Is All Heading

A few things are worth keeping up with as this continues to develop:

• Models are getting cheaper and faster: What required significant computing power a year ago is becoming affordable for smaller businesses.
• Images, audio, and video are becoming standard: Most of the major models can now create text documents, images, presentations, and audio.
• Open-weight models are becoming serious competition: Businesses can run capable models on their own infrastructure, which is raising real questions about data privacy and vendor dependence.

AI Agents Are Here, and They’re a Bigger Deal Than the Chatbots

Until now, these tools have worked one question at a time. You ask, they answer, you move on. AI agents are different. Give one a single instruction and it can carry out a whole chain of tasks on its own: book a meeting, write the follow-up email, pull the data you need, all without you managing each step. Tools like Gemini’s Deep Research and Claude’s Projects are early, working examples of this in real products, and the capability is developing fast.

For local businesses, the part that matters most is this: how AI tools find and describe businesses online is increasingly driven by mentions, citations, and consistent information across the web and not just traditional search signals.

Different AI tools update at different speeds, draw from different sources, and can hold onto wrong information about a business long after it’s been corrected.

That means the accuracy and consistency of your online presence aren’t just SEO issues anymore. They directly affect how AI tools describe and recommend you. Understanding how to structure your content so AI systems can find and use it is becoming part of the job.

Frequently Asked Questions

What exactly is a large language model?

Think of it as the AI engine running underneath tools like ChatGPT, Gemini, and Claude. It was trained on enormous amounts of text — books, websites, articles — and developed a working understanding of language along the way. That’s what allows it to write, answer questions, summarize documents, and explain complicated things in plain English. When you see AI features built into Google Search or other software, an LLM is powering them.

What’s the difference between an LLM and ChatGPT?

An LLM is the engine, and ChatGPT is one of the products built on top of it. OpenAI developed the GPT series of language models and built ChatGPT as the consumer-facing product on top. Google did the same thing with Gemini, where the model powers everything from Google Search to Google Workspace. Anthropic built Claude the same way. The model and the product you use every day are connected, but they serve different purposes.

Are ChatGPT, Gemini, and Claude basically the same thing?

They share a lot of surface-level similarities, but each one reflects different priorities from the companies that built them. Gemini is deeply woven into Google’s products and handles images and audio as well as text. Claude is particularly strong with long documents and coding. ChatGPT has the widest range of integrations and the largest existing user base. The landscape is also still shifting quickly, so the differences today may look different in six months.

What are LLM hallucinations, and do I need to worry?

Hallucinations are when an LLM gives you a confident, well-written answer that turns out to be factually wrong. It happens because these models generate what sounds most likely rather than looking up verified information. For tasks like drafting a first version of something or brainstorming ideas, this is easy to manage with a quick human review. For anything where accuracy really matters — legal, medical, or financial information — always double-check before acting on what you get back.

Do these tools know about current events?

Most LLMs were trained on information up to a certain date, so anything that happened after that point may be outside what they know. Some tools, like Perplexity and certain versions of ChatGPT and Gemini, can search the web in real time to stay current. If you’re using one of those, you’re in good shape for recent information. If not, it’s a good habit to verify anything time-sensitive through another source.

How do LLMs affect how customers find my business?

More than most people realize, and it’s growing. When someone asks Google a question, an LLM writes the AI Overview that appears above all the other results. When someone uses ChatGPT or Perplexity to find a plumber, a dentist, or a car dealer in their area, an LLM assembles that answer from whatever it can find about local businesses online. The things that have always driven good local SEO are exactly what these tools draw on: an accurate Google Business Profile, strong reviews, and consistent information across the web are all signals that influence who gets recommended.

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