# What is a language model

How a large language model (LLM) works, what tokens and context are, what a model does well and where it makes mistakes.

## In short

A **large language model** (LLM) is a neural network trained on a huge amount of text and code. Its
core skill is predicting the next piece of text. Everything else grows from that simple skill: answering
questions, summarizing, translating, writing code, reasoning step by step.

A model does not «look the answer up in a database» and does not «remember the internet» word for word.
It picks the most likely continuation of what it sees right now: your question, the rules, the files
passed to it.

## Tokens

A model reads and writes not in letters but in **tokens** — pieces of words. In English one token is
about three or four letters; Russian text usually splits into smaller tokens, so the same meaning costs
a few more tokens in Russian.

Tokens are also money: providers charge separately for the tokens the model reads (input) and writes
(output). Output is usually several times more expensive than input.

## Context

The **context** (context window) is how many tokens the model sees at once: the rules, the conversation
history, files and the question itself. Current models have windows from tens of thousands to a million
tokens. Whatever does not fit, the model does not see.

In SKILLOK the window is capped at 128 thousand tokens: a long history is compressed into a summary, and
large tool results are trimmed. That keeps answers fast and inexpensive.

## What a model does well

* Explaining and summarizing: a topic, an article, someone else's code.
* Writing drafts: text, code, tests, self-check questions.
* Working out structure: finding a flaw in reasoning, proposing a plan.
* Using tools: reading repository files, searching, opening issues, proposing changes.

## Where a model makes mistakes

* **It makes things up** («hallucinates»): it confidently names a fact, a link or a function that does
  not exist. Check what matters against the source.
* **It does not know recent events**: a model's knowledge ends at its training date. Fresh data has to be
  passed to it — as a file or through a web search.
* **It counts inaccurately**: long arithmetic is better left to code than to the model.
* **It depends on wording**: the more precise the question and the more relevant context, the better the
  answer.

## «Thinking» models and ordinary ones

Some models **reason** before answering — they write a hidden chain of steps. They solve math, logic
and code problems better but answer more slowly and cost more: the reasoning is paid for in tokens too.
A fast model without reasoning is enough for simple questions.

## What next

* [How to choose](https://skillok.org/docs/en/ai-models/choosing-a-model)
* [Connecting in SKILLOK](https://skillok.org/docs/en/ai-models/connecting-in-skillok)

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https://skillok.org/docs/en/ai-models/what-is-an-llm