# What a language model actually does
> **What you will learn**
> One layer deeper into "a program that guesses what word comes next" from
> [what AI is](/guide/ai-what-is-it). Knowing this lets you predict what it will be
> good at and what it will not.
## It really only does one thing
What a language model does is literally this one thing:
> **Look at the text so far and pick the next fragment.**
And then repeat.
```
Input: "The weather today"
1: "The weather today is"
2: "The weather today is nice"
3: "The weather today is nice, so"
4: "The weather today is nice, so a walk"
...
```
You have probably seen an answer appear one character at a time. **That is not a
visual effect — it is genuinely being produced that way.**
## So how does that become summarising and translating?
This is the key part. If it only guesses the next word, how does it summarise?
**You are not commanding it to summarise; you are creating a situation where a
summary is the only thing that can come next.**
```
(1,000 lines of text)
Summary of the above:
```
In that state, what comes next? In human writing, what follows "Summary of the
above:" is **a summary.** The model has seen that pattern countless times.
Translation is the same:
```
Korean: 오늘 회의는 취소되었습니다. (= today's meeting is cancelled)
English:
```
The natural continuation after "English:" is a translation.
> **So it is less "it understood the instruction" and more "it produced the
> natural continuation for that situation."** The results are similar, but this
> distinction explains the limits.
## What follows from this structure
### 1. What comes before is everything
The model picks the next fragment based only on **the text it is looking at.**
So:
- Describe the situation in detail → you get a continuation fitted to it
- Write nothing → you get the most generic continuation
That is why [getting the answer you wanted](/guide/ai-how-to-ask) says to give
the situation. It is not a trick — **it follows directly from how it works.**
### 2. The same question gives different answers
We said it "picks the next fragment," but there are several candidates and it
picks **probabilistically.**
```
After "The weather today":
is 45%
was 20%
looks 15%
...
```
Always picking first place makes writing stilted, so a little randomness is
built in. **That is why asking the same question twice gives slightly different
answers.**
This is design, not malfunction. It also means it is **unsuited to work
requiring identical results** (same input must always give the same output).
### 3. It is bad at arithmetic
Ask for `237 × 481` and it does not calculate — it produces **a number that
looks like a plausible answer.** That is where "right number of digits, wrong
value" comes from.
So in practice you make **a calculator do the calculating.** Rather than the
model doing it, you have it call a tool — the subject of the next chapters.
## What it is good and bad at
Knowing the structure makes this predictable.
| Good at | Why |
|---|---|
| Summarising, translating, reformatting | patterns common in human writing |
| Drafting | rich patterns for "writing in this situation" |
| Classification and tagging | give a few examples and it continues the pattern |
| Smoothing awkward sentences | producing natural continuations is its actual job |
| Bad at | Why |
|---|---|
| Exact arithmetic | it generates plausible numbers, it does not calculate |
| Current facts | it does not know past its training cutoff |
| Your company's internal information | it has never seen it |
| Identical output every time | it picks probabilistically |
**All four in the second table are solved with "tools."** Attach a calculator,
attach search, attach the internal database. That is
[tool use](/guide/ai-tool-use).
## Common misconceptions
### "The model understands my question"
The word "understand" causes confusion. From the results it looks like
understanding; in reality it produced **the most natural continuation in that
context.**
This distinction matters in practice because **natural and correct are not the
same thing.** A plausible but non-existent book title is the example — the
subject of the next chapter.
### "Won't a bigger model solve everything?"
Size increases the richness of patterns. But **it does not make the model know
facts it never saw.** No model, however large, knows your company's revenue.
That is not a size problem but a **connection** problem.
---
## Check yourself
**1. How does "summarise this" lead to a summary?**
Answer
Because **the natural continuation after "Summary of the above:" is a summary.**
The model has seen that pattern countless times in human writing. It is less
understanding and executing an instruction, and more producing what naturally
comes next in that situation.
**2. Why does the same question give slightly different answers each time?**
Answer
Because the next fragment is picked **probabilistically.** Always picking first
place makes writing stilted, so some randomness is built in. That is design, not
malfunction — but it is unsuited to work that needs identical output for
identical input.
**3. What is the common fix for the four things models are bad at?**
Answer
**Attaching tools.** Arithmetic to a calculator, current information to search,
internal information to a database. Making the model bigger does not fix it.
---
Next, the problem that follows inevitably from this structure →
[Why it is wrong so plausibly](/guide/ai-hallucination)