To understand this, you need to know two main concepts: "Tokenization" and "Pattern Learning".

Tokenization - Breaking words into smaller pieces

• Artificial intelligence does not read text as just chunks of sentences.

• It recognizes sentences by breaking them into smaller pieces. These are called tokens.

• Tokens can be words or parts of words. For example,

"I like kimchi."
→ ["I", "like", "kim", "chi", "."]

By breaking it down like this, it learns to remember and connect each piece.

Pattern Learning - Learning statistically what words follow which words

• AI reads books, websites, articles, etc., and
  calculates countless times what tokens frequently follow which tokens.

• For example, it learns that words like "delicious", "spicy", and "fermented" often come after the word "kimchi".

So when it receives a question,

• "Ah, in this topic, these words naturally follow."

• "In this context, it is natural to use this sentence structure."
  It retrieves patterns and,
  assembles a completely new sentence to respond.

Instant assembly, instant creation

• Artificial intelligence does not retrieve a fixed answer,

• but creates answers by linking tokens at every moment.

• This is why even the same question can have slightly different expressions each time.

The important thing is "probability".

• When deciding which word to use, it selects the "most probable" next word to continue the structure.

• This is why AI responses always appear to be "like natural sentences".

AI breaks text into small pieces (tokens) and learns billions of times what pieces follow which, to assemble the most natural answer in real-time.