Tiny Text Predictor Model (How AI Predicts Text)

Modern natural-language models use an architecture called a Transformer. A Transformer predicts the next token (a word or a piece of a word) based on the input it receives, using patterns it learned during training. During training, the model is exposed to millions of examples of text and learns which words or tokens are most likely to follow others in different contexts, based on probability. 

For example, if the model sees the phrase “An electron has a …”, it has learned from many texts that “negative charge” is a very probable continuation. This is similar to how humans learn patterns of language from their environment and can often predict what word comes next. However, unlike the model, humans do not choose the next word purely from learned patterns—they rely on reasoning, intention, context, and their own understanding of what they want to express. Humans select the word that best conveys their thoughts, not simply the word with the highest statistical likelihood.

This demo learns simple word patterns from the training text below and tries to predict how sentences continue. Type a short prompt and click Generate, or edit the training text and rebuild the model.

Your prompt

Max extra words: 5 10 20

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Model output:

Nothing yet – try a prompt above.

Training text (editable, temporary)

The model learns from this text using a simple 3-gram language model (uses up to the last two words). You can paste your own text here and click Rebuild model. Changes live only in your browser.

Every morning the city slowly wakes up and people begin their daily routines. Some people make coffee, others prepare breakfast, and many check their phones before they leave the house. The streets become busy as cars, buses, and bicycles move in different directions. When the weather is good, people like to walk to work or to school because the fresh air helps them feel more awake. At work, employees talk to their colleagues, answer emails, and attend meetings. A good manager listens carefully, gives clear instructions, and supports the team when there is a problem. If someone makes a mistake, they try to fix it and learn from the situation instead of arguing. When people cooperate and communicate politely, projects are finished faster and everyone feels more comfortable. During the day, students study various subjects such as mathematics, science, history, and English. They read texts, write answers, and ask questions when they do not understand something. A helpful teacher explains ideas in different ways and encourages students to practice. If a student works regularly and reviews their notes, they can improve their grammar, expand their vocabulary, and speak more confidently. In conversations, people often use small words to connect their ideas. They say "and" to add information, "but" to show contrast, and "because" to give a reason. For example, they might say, "I wanted to go outside, but it was raining," or "She stayed at home because she was very tired." These simple words help sentences sound natural and make the meaning easier to follow. In the evening, families usually relax together. Some people cook dinner, wash the dishes, and clean the kitchen. Others sit on the sofa, watch a movie, or talk about their day. Children may finish their homework before they play games or go to bed. Before sleeping, many people think about their plans for tomorrow and hope that the next day will be calm and successful. Sometimes people feel worried or stressed, but simple habits can make life easier. It is helpful to sleep enough hours, drink water, and take short breaks during work. When a person feels overwhelmed, they can talk to a friend, go for a walk, or write down their thoughts. If they practice these healthy habits regularly, they will usually feel more balanced and ready to handle new challenges.

Model loaded from default text.

This is an educational toy, not a neural network or real Transformer. It uses word statistics (n-grams) to guess what comes next.