A large language model (LLM) is a type of AI that has read most of the public internet and learned to predict what word should come next in a sentence. That sounds simple. The trick is that by getting very good at "next word prediction," it ends up able to write emails, answer questions, draft code, summarize documents, and reason about your business. Claude, ChatGPT, Gemini, and Grok are all LLMs. Every AI agent on the market today has an LLM as its brain. Knowing roughly how LLMs work helps you trust the right tasks to them and refuse the wrong ones.
What is a large language model?
A large language model is software trained on enormous amounts of text (books, websites, code, conversations) until it can generate human-like text in response to a prompt. "Large" refers to the model's size. Most modern LLMs have between 70 billion and 2 trillion parameters, the internal knobs that store everything the model learned during training. "Language" means its native medium is text, though most modern LLMs can also handle images and audio now.
How an LLM actually works (the simple mental model)
Imagine the LLM is a very fast reader. It has read essentially every book, every Wikipedia article, every Stack Overflow answer, every blog post, and a lot of code that ever appeared online. As it read, it learned patterns. Which words usually follow which words. How a paragraph that starts a certain way usually ends. How a question gets answered.
When you give an LLM a prompt, here is what happens in plain terms:
- It breaks your prompt into small chunks called tokens (roughly: words and word fragments).
- It feeds those tokens through a massive web of pattern-matching.
- It predicts the most likely next token.
- It adds that token to the end and predicts the next one.
- Repeat until it has produced a complete response.
That is the entire trick. Predict the next token. Everything an LLM can do, from writing a customer support reply to debugging Python, comes from doing this prediction step billions of times across very large training data.
The reason this works is that the universe of writing humans have done is enormous and patterned. Predicting the next word in a sentence about plant nutrition is fundamentally the same kind of work as predicting the next word in a sentence about contract law. Both require understanding context. Both reward a model that has read a lot.
Training vs inference (why this matters for your wallet)
There are two phases in an LLM's life. Knowing the difference saves you money.
Training is the process where the model learns. The company that makes the LLM (Anthropic for Claude, OpenAI for ChatGPT, Google for Gemini, xAI for Grok) takes a giant pile of text data and runs it through a learning algorithm. This costs tens to hundreds of millions of dollars in computing power. It happens once and produces a single trained model that gets shipped to users.
Inference is the process where the model generates a response when you actually use it. Every time you type a prompt and get an answer, that is an inference call. Inference is much cheaper than training but still costs real money, usually fractions of a cent to a few cents per call.
Why this matters for your business: when you deploy an LLM-powered agent, you are not paying for training. You are paying for inference. Costs scale roughly with how many tokens (words) you send and receive. A customer-support agent that handles 100 tickets a day costs more than one that handles 10. A business-intelligence agent that reads 50 pages of context per question costs more than one that reads two paragraphs.
Why LLMs sometimes make things up (hallucinations)
LLMs do not "know" anything in the way you do. They predict what text should come next based on patterns. If you ask an LLM "what was your boss's middle name?" it will sometimes invent one, because making up a plausible name is statistically what a confident-sounding answer would look like.
This is called hallucination. It is the single biggest failure mode of LLMs and the reason you cannot just deploy one and walk away from it.
Common hallucination patterns
- Inventing book titles, paper citations, or expert quotes that sound real.
- Misremembering specific numbers (dates, prices, dosages, addresses).
- Making up plausible-sounding case law, regulations, or statistics.
- Confabulating internal procedures or policies the model has never actually seen.
How real AI agent builds reduce hallucinations
- Ground the model in real data. Do not ask it to remember; give it the document and tell it to summarize.
- Use tool calls. Do not ask the agent "what is in stock right now?" Have it call your inventory API and return the actual number.
- Add a verification step. For high-stakes outputs, run a second LLM call that checks the first one's claims against the source data.
- Always show your work. If the agent cites a fact, link to the source so a human can spot the lie at a glance.
None of this makes hallucination disappear. It makes hallucination much less expensive.
The major LLM families (and where each one quietly leads)
The big four right now. We use Claude as the default for many agent builds, but all of them are useful for different jobs.
| Family | Maker | Notable strengths | Where it leads |
|---|---|---|---|
| Claude (Sonnet, Opus, Haiku) | Anthropic | Reasoning, code, long-context (200K+ tokens), careful tool-use | Multi-step workflows, agent backbones, careful drafting |
| ChatGPT (GPT-4o, GPT-5) | OpenAI | Broad capability, image and voice modes, plugins, custom GPTs | General-purpose chat, ecosystem breadth (apps, voice, custom data) |
| Gemini (1.5, 2.0, 2.5) | Massive context window (1M+ tokens), image generation, Google integration | Document-heavy tasks, multimodal (image, video, audio) | |
| Grok (3, 4) | xAI | Real-time info from X, less filtered tone, fast | Real-time research, conversational dynamics |
Other models worth knowing exist. Meta's Llama is the leading open-source alternative; Mistral is the European one; DeepSeek and Qwen are notable Chinese models. You will run into Claude, ChatGPT, Gemini, or Grok in 95% of side-by-side comparisons, so start there.
How LLMs power AI agents
An LLM by itself is just a text-generating box. You type; it responds. That is the whole interaction.
An AI agent is what you build on top of the LLM. Same brain, but now with:
- Tools it can call (an API request, a database lookup, a code run, a Slack post)
- Memory of past conversations, customers, decisions
- A goal ("handle this support ticket end to end") instead of a single prompt
- A loop that lets it take an action, see the result, and decide the next action
The same Claude that answers your question in a chat interface can also be the brain inside an agent that drafts customer replies, looks up orders in Shopify, and writes back to a database. Same model. Different scaffolding around it.
If you remember nothing else: the LLM is the brain. The agent is the brain plus a body. Without an agent layer, the LLM just talks. With one, it works.
What an LLM cannot do (and why this matters)
Honest limits, because nobody else will tell you them up front:
- LLMs do not learn from your conversation. When you tell ChatGPT "remember I prefer concise replies," it remembers within that conversation but forgets when you start a new one, unless the product wrapping the LLM adds a memory feature.
- LLMs cannot reliably do math without a calculator tool. They will get arithmetic wrong once the numbers get big or the steps get tricky. Always give them a calculator if math matters.
- LLMs cannot reliably look up current information. Their training data has a cutoff date, often months to a year behind today. To get current info, the agent has to use a search or API tool.
- LLMs cannot tell you when they do not know. They will confidently make something up before they say "I am not sure." This is the hallucination problem we covered earlier.
- LLMs cannot execute code or change anything in the world by themselves. They generate text. An agent layer is what gives them the ability to actually do things.
Understand these limits and you build agents that work in production. Ignore them and you build agents that embarrass you on Reddit.
Frequently asked questions
Is ChatGPT an LLM or an AI agent?
ChatGPT is a chat interface built on top of an LLM (GPT-4o, GPT-5, etc.). It has some agent-like features now (web browsing, image generation, code execution), but at its core it is a chat product. The LLM is the brain; ChatGPT is the wrapper around it.
How big is the difference between Claude and ChatGPT?
On consumer tasks, modest. On code, agent workflows, and long-context reasoning, Claude has been quietly ahead for the last 18 months. On image generation and voice modes, ChatGPT has the lead. For building business agents, we default to Claude. For consumer chat or one-off creative work, ChatGPT is fine.
Do I need to pay for an LLM API to use AI in my business?
Eventually yes, for production reliability. Free chat interfaces (claude.ai, chat.openai.com) are great for personal use. To deploy an agent that runs reliably at business volume, you use the API and pay per token, typically $20 to $500 a month per agent depending on volume.
Are LLMs going to get a lot better next year?
Probably yes. Frontier model capability roughly doubles every 12 to 18 months. A much better LLM usually does not change the architecture of a well-built agent. The agent still drafts, the human still approves; the model just gets faster and cheaper at it. Build with replaceable model assumptions.
Can I run an LLM on my own server?
Yes, with open-source models like Meta Llama or Mistral. Trade-off: lower per-token cost over the long term, but high upfront infrastructure cost and lower capability than frontier models. For most businesses under $10M revenue, the API path is the right call.
Key takeaways
- An LLM is a type of AI that predicts the next word in a sequence, trained on most of the public internet. That simple trick produces the appearance of reasoning across nearly any text-based task.
- The big four LLMs are Claude, ChatGPT, Gemini, and Grok. We default to Claude for agent work; the others have specific strengths.
- Every AI agent is an LLM plus an agent layer (tools, memory, a goal, a loop). The LLM by itself is just a chat box.
- LLMs hallucinate. You build around this by grounding the model in real data, using tools for facts, verifying high-stakes outputs, and showing the work.
- LLMs cannot reliably do current-info lookup, math, or self-correction on their own. The agent layer covers those gaps with the right tools.
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