When you type a message on your phone or laptop, autocorrect is quietly making predictions in the background. It does not understand language the way humans do. Instead, it works by spotting patterns.
At its core, autocorrect uses large collections of text to learn which words often appear together. For example, if many people type “looking forward to”, the system learns that “to” is very likely to follow “forward”. Over time, it builds a probability map of word sequences. When you start typing a sentence, autocorrect checks this map and suggests the word that is most likely to come next.
Your personal typing habits also matter. Autocorrect watches which words you use often, how you spell names, and which suggestions you accept or reject. If you regularly type “meeting” after “team”, your device starts favouring that choice. This is why two people typing the same sentence may see different suggestions.
Modern autocorrect systems use machine learning models called language models. These models look at more than just the previous word. They consider the context of the entire sentence so far. If you type “I left my keys on the”, the system knows that a noun related to location is likely, such as “table” or “desk”, rather than a random word.
Autocorrect also relies on dictionaries and grammar rules. This helps it avoid impossible combinations and fix obvious spelling errors. However, it can still make strange mistakes because it chooses what is statistically likely, not what you personally mean.
Privacy is an important concern. Many systems now process predictions directly on your device rather than sending every word to external servers. This helps keep private messages private while still allowing autocorrect to improve over time.
In short, autocorrect guesses the next word by combining patterns from large amounts of text, your own typing behaviour, and probability based predictions. It feels intuitive, but it is really a fast and constant calculation happening with every keystroke.