WiseWhys

You type a question into a chatbot, hit enter, and a few seconds later you get an answer that actually makes sense. No dictionary lookup, no human typing back at you—just a program reading your words and responding to them. So how does a chatbot actually read your sentence? What is happening between the moment you hit enter and the moment the reply appears?

The short answer: the chatbot never reads your sentence the way you do. It breaks it into pieces called tokens, turns those pieces into long lists of numbers called embeddings, and then uses a process called attention to figure out how all those numbers relate to each other. There is no dictionary of meanings anywhere inside it. It is numbers, patterns, and probability, all the way down.

Step one: breaking your sentence into tokens

Before a chatbot can do anything with your sentence, it has to chop it into smaller chunks called tokens. A token can be a whole word, a piece of a word, or even a single punctuation mark. The word “understanding,” for example, might get split into pieces like “under,” “stand,” and “ing.”

Most modern chatbots work with a vocabulary of somewhere between 30,000 and 100,000 possible tokens. Splitting words into smaller pieces like this lets the model handle words it has never seen before—including typos, made-up words, or names—by rebuilding them out of familiar fragments, the same way you could sound out an unfamiliar word by breaking it into syllables.

Step two: turning words into numbers

Computers cannot do math on the word “dog.” So each token gets converted into an embedding: a long list of numbers, often several hundred or even a few thousand values long. Think of each embedding as a coordinate that places the token somewhere in a giant, invisible map of meaning.

Tokens that mean similar things end up close together on that map. “Cat” and “kitten” land near each other. “Cat” and “lion” are a little farther apart but still in the same general neighborhood, since they are both animals. “Cat” and “umbrella” end up nowhere near each other at all. The model never memorized a definition of “cat”—it learned this position by seeing the word used millions of times across huge amounts of text, and noticing which other words tended to show up around it.

Step three: figuring out which words matter most

Here is the part that actually lets a chatbot handle context. Take the sentence: “The trophy didn’t fit in the suitcase because it was too big.” What does “it” refer to—the trophy or the suitcase? You know instantly it means the trophy. A chatbot figures this out using a mechanism called attention, introduced in a landmark 2017 research paper called “Attention Is All You Need.”

Attention lets every token in a sentence look at every other token and ask, essentially, “how relevant are you to me?” The model assigns a weight to each of those relationships. When processing the word “it,” attention lets the model weigh “trophy” much more heavily than “suitcase,” based on patterns it absorbed from enormous amounts of training text. Do this across an entire paragraph, and the model builds something like a web of connections between every word and every other word—linking pronouns back to the nouns they replace, verbs to their subjects, and adjectives to the nouns they describe.

• Attention connects pronouns like “it” or “she” back to the noun they stand in for.
• It links verbs to the subjects performing the action, even across a long sentence.
• It lets the model weigh earlier parts of a conversation, not just the most recent sentence, when forming a reply.

This is the job of the transformer, the architecture almost every modern chatbot is built on. Stack enough of these attention layers on top of each other, train them on a large enough slice of the internet, books, and articles, and the model gets remarkably good at tracking who did what to whom, even in long or complicated sentences.

Step four: predicting the next word, over and over

Once the model has built its numerical picture of your sentence, how does it write a response? One token at a time. At each step, the model looks at everything so far—your question plus whatever it has already written—and calculates a probability for every token in its vocabulary: how likely is each one to come next? It picks one of the most likely options, adds it to the sequence, and repeats the entire process to choose the next token.

This means a chatbot is not retrieving a stored answer from somewhere. It is generating a reply one small piece at a time, guided by patterns learned from an enormous amount of text, checked at every step against the tokens, embeddings, and attention weights built from your original question.

So does it actually “understand” language?

Not the way a person does. A chatbot has no senses, no memories of its own life, and no concept of what a “cat” actually looks or feels like. What it has is an extremely detailed statistical map of how words relate to other words, built from tokens, embeddings, and attention. That map is good enough to track grammar, context, and meaning well enough to hold a convincing conversation—but underneath, it is pattern recognition and probability, not comprehension in the human sense.

Summary

A chatbot reads language in four steps: it breaks your sentence into tokens, converts those tokens into number-based embeddings, uses attention to weigh how every word relates to every other word, and then predicts a reply one token at a time. There is no dictionary and no human-style comprehension inside the machine—just tokens, vectors, and probability, arranged by a transformer architecture that has seen an enormous amount of human writing.

Sources:

IBM – “What Is NLP (Natural Language Processing)?”

Vaswani et al. – “Attention Is All You Need”

Dremio – “How LLMs Work: Tokens, Embeddings, and Transformers”

Coursera – “What Is Attention Mechanism?”