In a nutshell
Models don't read whole words or single letters but 'tokens' — subword chunks. Byte-pair encoding learns them by starting from characters and repeatedly merging the most frequent neighbouring pair into a new token. Common pieces like 'ing' end up as single tokens while rare words stay split, balancing vocabulary size against sequence length.