# Large language model (LLM): how does it work?

> What a large language model is, how it writes by predicting tokens, where it helps a service SME, a worked example and the limits to know before adopting one.

Updated on October 15, 2026 · https://app.pilotiko.com/en/glossary/llm

A large language model (LLM) is a very large language model trained on huge collections of general text and designed to handle a wide range of language tasks: drafting, summarizing, translating and reasoning. It writes by predicting, piece by piece, the most likely continuation of the text it has been given, which is usually right but not always.

## In short
- LLMs typically have billions, even trillions, of parameters.
- They run on remote servers because of the computing power they need.
- They predict the next token: often accurate, sometimes confidently wrong.
- Used well, they produce a first draft that a person checks.

## How does an LLM produce an answer?

NIST's Generative AI Profile (NIST AI 600-1) sums up the mechanism: generative models produce outputs that approximate the statistical distribution of their training data, and an LLM, for instance, predicts the next token or word in a sentence. That prediction often yields accurate, consistent text, but it can also yield output that is factually wrong or internally inconsistent [1]. This is where [AI hallucinations](https://app.pilotiko.com/en/glossary/ai-hallucination) come from.

Quebec's OQLF terminology entry adds two practical facts: these models generally have billions or even trillions of parameters, and they need so much computing power that they are hosted on remote servers [2]. When you type a question into an assistant, your text travels to those servers.

## What is the difference between an LLM and an AI agent?

On its own, an LLM produces text. Anthropic describes agents as LLMs that use tools in a loop based on feedback from their environment [3]. Give a model tools, data and a multi-step goal, and you have the basis of an [AI agent](https://app.pilotiko.com/en/glossary/ai-agent).

In French, the OQLF prefers grand modèle de langage, abbreviated GML, and accepts LLM because its use is limited to a highly specialized field [2]. You will see both on bilingual Quebec sites.

## Where does an LLM help a service business?

| Use | Example | Human check |
|---|---|---|
| First draft | A post about a new snow-removal service | Review facts, prices and tone |
| Summary | Digest of 30 customer comments | Spot-check against the originals |
| Translation | English version of a services page | Have a bilingual person proofread |
| Sorting | Triage of web form requests | Review the ambiguous ones |
| Factual answer | Opening hours, prices, lead times | Always give the model the source |

## How much drafting time can an LLM shift?

**Example: a Saguenay landscaping company that posts every week**

Assumption: the owner writes 8 posts a month herself. With an LLM, she starts from a draft and edits it.

- Posts per month : 8
- Writing time without help, per post : 45 min
- Time to review and fix a draft : 15 min
- Difference per post : 30 min
- Difference per month (8 × 30 min) : 4 h
- **= About 4 hours a month move from writing to reviewing; fact-checking remains essential.**

## What mistakes do businesses make with LLMs?

- Treating it like a database: it predicts plausible text and does not necessarily look anything up.
- Pasting customers' personal information into it without knowing where it is processed.
- Publishing without review; [human approval](https://app.pilotiko.com/en/glossary/human-in-the-loop) before anything goes out prevents expensive mistakes.
- Asking for figures without supplying yours: it will invent convincing ones.
- Forgetting that generative engines read your site too: clear content supports your [GEO](https://app.pilotiko.com/en/glossary/geo) visibility.

## How does Pilotiko use large language models?

Pilotiko uses them to draft posts and keep an editorial calendar, and to let its AI agents work from the business's real data. You approve every post. Automatic publishing to LinkedIn is being commissioned; other networks get guided copy-and-paste.

## Frequently asked questions

### What is the difference between an LLM and artificial intelligence?

Artificial intelligence is the broad field. A large language model is one specific kind of model, specialized in language. Today's writing assistants are built on this kind of model.

### Does an LLM understand what it writes?

It calculates the most likely continuation of a text from what it learned. The result can be accurate and well reasoned, but the model can also state something false with confidence. Check any fact that matters.

### Why is it called a large language model?

Because of its size: billions or even trillions of parameters and enormous training sets. That size is also why these models run on remote servers rather than on an office computer.

### Can I use an LLM to answer customers?

Yes, as long as you give it the right information, such as prices, hours and policies, and keep a human sign-off for any commitment. A model left without sources may give a plausible but wrong answer.

### Does LLM-written text hurt search rankings?

Google asks site owners to consider who created content, how it was made, including any use of AI, and why: it should exist mainly to help visitors [4]. Generic, unchecked text does poorly on those questions linked to [E-E-A-T](https://app.pilotiko.com/en/glossary/e-e-a-t). A draft enriched with your real examples, prices and experience is far more useful.

## Sources

1. NIST, « NIST AI 600-1 : Generative Artificial Intelligence Profile », https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf (accessed on 2026-10-15)
2. Office québécois de la langue française (Vitrine linguistique), « grand modèle de langage (GDT) », https://vitrinelinguistique.oqlf.gouv.qc.ca/fiche-gdt/fiche/26570880/grand-modele-de-langage (accessed on 2026-10-15)
3. Anthropic, « Building effective agents », https://www.anthropic.com/engineering/building-effective-agents (accessed on 2026-10-15)
4. Google Search Central, « Creating helpful, reliable, people-first content », https://developers.google.com/search/docs/fundamentals/creating-helpful-content (accessed on 2026-10-15)