TL;DR

Modern chatbots use Large Language Models (LLMs) trained on billions of words. They predict the most likely next word in a conversation, over and over, creating responses that feel natural. They don't "understand" like humans—they're amazing pattern matchers.

Why it matters

Chatbots are everywhere: customer service, writing assistants, coding help, tutoring. Knowing how they work helps you use them effectively and spot their limits.

The basic flow

When you ask a chatbot a question:

  1. You type a prompt ("What's the weather like in Paris?")
  2. The chatbot processes it (turns your text into numbers it can work with)
  3. It predicts a response (one word at a time, based on patterns it learned)
  4. You see the answer ("Paris is typically mild in spring...")

All of this happens in seconds.

How does it "understand" my question?

It doesn't—at least not the way you do. Here's what really happens:

  • Tokenization: Your sentence is broken into chunks called tokens (roughly words or parts of words)
  • Embedding: Each token is converted into a list of numbers (a vector) that represents its meaning
  • Context: The model looks at all the tokens together to understand the full context

Jargon: "Token"
A piece of text the AI processes—usually a word or part of a word. "Chatbot" might be one token, or it might be split into "chat" and "bot."

Jargon: "Context Window"
How much text the chatbot can "remember" at once. If the context window is 4,000 tokens, it can consider about 3,000 words of conversation history. Older messages get "forgotten."

Predicting the next word

The core trick: the chatbot predicts the most likely next word, then the next, then the next—building a sentence word by word.

Example:

  • Input: "The capital of France is"
  • Model thinks: "Paris" is very likely, "London" is not
  • Output: "Paris."

It does this using billions of parameters—numbers that encode patterns learned from training data.

Training: Where the magic happens

Before a chatbot can chat, it goes through training:

  1. Data collection: Gather massive text datasets (books, websites, articles)
  2. Learning patterns: The model reads examples and learns which words follow which
  3. Fine-tuning: Adjust the model to be helpful, safe, and accurate (using human feedback)

The result: a model that can complete sentences, answer questions, write code, and more—based purely on patterns it learned.

How does it stay on topic?

The chatbot uses context from your conversation. It doesn't have memory like you do, but it can see the recent messages in the conversation (up to its context window limit).

  • Short conversation: Easy to stay on topic
  • Long conversation: Older messages might "fall off" and be forgotten
  • Complex topics: It might lose the thread or mix up details

What about wrong answers?

Chatbots sometimes generate confident-sounding nonsense. This is called a hallucination.

Why it happens:

  • The model is predicting plausible text, not checking facts
  • It fills gaps with its best guess, even if it's wrong
  • It doesn't know what it doesn't know

How to avoid it:

  • Double-check important facts
  • Ask for sources or evidence
  • Use chatbots as a starting point, not the final authority

The role of prompts

Your prompt (the question or instruction you give) shapes the answer. A vague prompt gets a vague answer. A clear, specific prompt gets a better response.

Example:

  • Vague: "Tell me about AI."
  • Better: "Explain how chatbots predict the next word, in simple terms."

See our guide Prompting 101 for tips on asking better questions.

Can it learn from our conversation?

Not really. Most chatbots don't "learn" from individual conversations. They're static—trained once, then deployed. Your chat doesn't change the model itself.

Some systems might store your conversation history to improve context within a session, but they're not learning new facts or skills from you.

Key terms (quick reference)

  • LLM (Large Language Model): AI trained on massive text to generate language
  • Token: A chunk of text (word or part of a word) the AI processes
  • Context Window: How much text the chatbot can "see" at once
  • Parameters: Numbers inside the model that determine behavior
  • Hallucination: When AI generates false or nonsensical information
  • Prompt: Your question or instruction to the chatbot
  • Embedding: Turning text into numbers the AI can work with

Use responsibly

  • Don't share secrets: Assume your chat might be stored or reviewed
  • Verify facts: Especially for medical, legal, or financial advice
  • Be clear: Better prompts = better answers
  • Know the limits: Chatbots are tools, not oracles

What's next?

  • Prompting 101: Learn to craft effective prompts
  • Evaluating AI Answers: Spot hallucinations and check for accuracy
  • Embeddings & RAG: How chatbots search knowledge bases
  • AI Safety Basics: Use AI responsibly in your team