You type a question, and a smart-sounding answer appears in seconds. It feels like magic, but the idea behind it is surprisingly simple.
It predicts what comes next
ChatGPT, Claude and Gemini are all LLMs, or Large Language Models. An LLM was trained on huge amounts of text, and it learned one skill very well: given the text so far, what is the most likely next piece?
Think of your phone’s keyboard suggesting the next word. An LLM does the same thing at a vastly bigger scale, and it keeps going, piece by piece, until the answer is complete.
What is a token?
The model doesn’t read whole words. It reads tokens, small chunks of text. A short word is often one token, and a long word may be split into two or three. This matters for two practical reasons:
- Every model has a limit on how many tokens it can handle at once (its “memory” for the conversation).
- Many AI services are priced by tokens.
Why does it sometimes get things wrong?
Because it is optimized to produce text that sounds right, not text that has been verified. When it doesn’t know something, it can still produce a confident, fluent, and completely wrong answer. This is called a hallucination.
What this means for you
- Be specific. The more context you give, the better the predictions get.
- Give examples. Show the format or tone you want and the model will follow it.
- Verify what matters. Dates, numbers, quotes, medical and legal information should be checked against a reliable source.
- Start fresh when it drifts. In a very long chat, early details can get lost. A new conversation with a short summary often works better.
Where to go next
Once you understand this idea, concepts like temperature (how adventurous the model is) and RAG (letting it look things up in your documents) become easy to grasp. We walk through each with stories and simulators, not formulas, in the How It Works level of Amoozyar AI. The first level is free, forever.