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Intermediate

What is an LLM

Full name: Large Language Model

Also known as: large language model

Definition

An LLM (large language model) is AI trained on masses of text to predict the next piece of a sentence. That is how chat tools write answers, right or wrong.

Why it matters

Most AI tools a small business meets, from chat assistants to WhatsApp bots and email drafting, run on an LLM.

Common mistakes

Trusting a fluent answer without checking it.

Related terms

Worked exampleIllustration

"Are you open on Friday?" at a fictional pilates studio

A fictional neighbourhood pilates studio's WhatsApp assistant runs on an LLM. The exchange is invented for this example.

  1. No hours given

    The assistant's instructions do not include the studio's Friday hours.

  2. One piece at a time

    It writes "Yes," then "we are open", "on Friday", "until", "14:00".

  3. Why 14:00

    Each piece was a likely next one, and 14:00 is a likely Friday closing time in texts like this. It is not the studio's.

  4. The fact

    The studio closes at 13:00 on Friday.

  5. Fixed

    After the hours are added to its instructions, the same question gets 13:00.

It wrote what was likely. It became right only when it was given the fact.

Illustration

One piece at a time

CustomerAre you open on Friday?

AssistantYes,we are openon Fridayuntil14:00

Step 5 of 5: the likely next pieces

  • 14:0034%
  • 13:0022%
  • 15:0017%

14:00 had only 34%, and the studio’s real 13:00 came second at 22%. The reply still reads as sure as a fact.

An LLM writes an answer one piece at a time, each time picking a likely next piece. Likely is not the same as right.

Definition

An LLM, a large language model, is a model trained on a vast amount of text to predict what comes next in a piece of writing. Given a prompt, it writes the answer one token at a time, a word or a piece of a word, each time choosing a likely next piece. That is why it writes fluently in many languages, including Hebrew, and also why it can sound right and be wrong. An LLM knows only the text it was trained on, up to a cutoff date, unless the tool around it searches the web or reads your files. ChatGPT, Claude, Copilot and Gemini are examples of products built on LLMs.

Why it matters

Knowing that it predicts likely text explains both its strength and its main risk: a fluent answer is not a checked answer. It also explains the fix: give it your facts, and check what reaches customers.

Directly related: Token, Context Window, Hallucination.

Improvement tips

  • Give the model the facts it needs in the prompt or in attached documents, instead of relying on what it learned in training.
  • When an answer will reach a customer, ask for a short answer with the source of each fact.
  • Check your plan's data settings to see whether your conversations may be used to train future models.
  • Before switching models or tools, test them on ten questions your customers really ask.

Common mistakes

  • Assuming the model knows today's news, your prices or your opening hours without being given them.
  • Pasting customer ID numbers, medical details or card details into a public AI tool.
  • Treating the model as if it understands the business, when it predicts text from patterns.

Related terms

AI and DataIntermediate

Token

A token is a piece of a word that an AI language model reads and writes. Usage is counted and billed in tokens, and the same text takes more in some languages.

AI and DataIntermediate

Context Window

A context window is how much text, in tokens, a model can see at once: instructions, files, the chat so far and its reply. What falls outside, it cannot use.

AI and DataBeginner

Hallucination

An AI hallucination is an answer an AI model makes up and states with confidence, such as an invented price, rule or source, because it predicts likely words.

AI and DataBeginner

Prompt

A prompt is the instruction you give an AI tool: the question, the task, the context and the format you want. A clearer prompt gets a more useful answer.

AI and DataAdvanced

RAG

RAG (retrieval-augmented generation) is a method where AI answers from your documents: it first finds the relevant passages, then writes the answer from them.

AI and DataBeginner

Generative AI

Generative AI is AI that creates new text, images, audio or code from a request. What it writes is a likely draft built from patterns, not a checked fact.

Quick check

Why can an LLM give a fluent answer that is wrong?

Choose an answer

Frequently asked questions

What is an LLM in simple words?
A program trained on a vast amount of text to guess the next piece of a sentence. Repeating that step, one piece at a time, it writes whole answers. Chat tools such as ChatGPT, Claude, Copilot and Gemini are built on LLMs.
What does LLM stand for, and what is it in Hebrew?
Large language model. In Hebrew it is מודל שפה גדול, and many Israelis simply say LLM. The name is about scale: these models learn from very large amounts of text.
Does an LLM answer in Hebrew?
Yes. LLMs are trained on text in many languages and can read and write Hebrew. The same content usually takes more tokens in Hebrew than in English, so it uses more of the context window and more of a usage based plan.
Does an LLM know my business?
No, unless you give it the information. It knows the text it was trained on, up to a date, and nothing about your hours, prices or policies. Put those facts in its instructions or connect it to your documents.
What is a knowledge cutoff?
The date up to which the model's training text reaches. After it, the model knows nothing unless the tool searches the web or reads your files. Anthropic's documentation, for example, lists a reliable knowledge cutoff and a separate training data cutoff for each model. The dates change with every release, so check the tool you use.
Why does an LLM sometimes invent things?
Because it predicts what sounds right, and sometimes what sounds right is false. It writes the invented part in the same confident tone. That is called a hallucination, and the answer is to give it the facts and check what reaches customers.
How do I pay for an LLM?
By use or by subscription. Tools charge per request, per amount of text measured in tokens, or within the limits of a plan. This page gives no prices, because they change often: check the vendor's own pricing page.
Is an LLM safe to use with company records?
It depends on your plan and your settings, not on the model. Check what your plan does with your conversations and whether they may be used for training. Never paste customer ID numbers, medical details, card details or anything you would not send by email to a stranger into a public AI tool. Israel's Privacy Protection Law, 1981, as amended by Amendment 13, in force since 14 August 2025, governs collecting, keeping and using personal details, including details you paste into or upload to an AI tool. Educational material, not legal or tax advice. Check your own case with a lawyer or an accountant.
Does an LLM think like a person?
No. It has no beliefs or intentions. It seems smart because it learned the patterns of a vast amount of writing and matches your question to them. That makes it strong at drafting and summarising, and it is why its answers need checking.
Which LLM should I choose?
Test instead of guessing. Run ten questions your customers really ask through the tools you are considering, and compare the answers for accuracy, tone and length. For simple tasks a smaller, faster model is often enough.
How do I connect an LLM to my own data?
Many tools let you attach files or connect a folder, and some search those files for the relevant passages before answering, which is called RAG. A setup that reads your own systems directly usually needs someone technical.
Do I need technical training to work with an LLM?
No. You steer it with prompts written in plain language, including Hebrew. What helps most is a clear task, the right facts and an example of a good answer.

Sources: Anthropic, "Models overview", Claude documentation, the rows "Reliable knowledge cutoff" and "Training data cutoff" (platform.claude.com/docs/en/about-claude/models/overview), read 2026-09-25, Privacy Protection Authority, Amendment 13 questions and answers (gov.il/he/pages/tikun13_qa), read 2026-08-31

Last reviewed: 2026-09-25

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