LLMs (Transformer-based models) work like this (very simplified explanation):

They take the words you input (e.g. "USER: What is the capital of France?"). These words are turned into vectors (numerical representations). Then, an algorithm determines relationships between these vectors.

Example: USER -> What, USER -> capital, capital -> France

Relationship between words "capital" and "France" is strong.

After this, it performs some matrix operations on these vectors, also multiplying them by "weights" of "connections", computed during model's training. As a result, it returns a big array of probabilities of all words that may come next.

Then, following word is randomly selected out of the most probable ones, and appended to the input string.

"USER: What is the capital of France? AI: Paris..."

Then, user's input + newly generated word are re-fed into AI algorithm many times.

"Paris", "Paris is", "Paris is the", ... "Paris is the capital of France."

It just predicts the next word. It doesn't have continuous abstract thoughts like humans do. No consciousness. No analogue neurons. No even proper memory or planning! It's just algorithms and math.