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This is a very complex topic, I'd probably start with 'neural network' (this is the model used by this kind of A.I.) and look at some simple diagrams to get an idea for how this works on a conceptual level. You can ignore the details of the math, its relatively unimportant. You want to understand how neurons (or 'perceptrons') get inputs, give an output based on weights assigned to input values, and you train the AI by giving it curated examples, so when the output is wrong, it does some math, and adjusts how much it 'weights' the inputs to get closer to the answer you want. If you give it enough examples, it will be able to give correct outputs for inputs it has never seen, because you've trained the weights for each input well enough for the AI to learn mathematical relationships between input and output. Now that is the most basic type of configuration, and they get far more complex, but thats the basics of how a neural network works, from there you can start to look at how they handle different types of inputs, such as language models. For those I'd start with 'natural language processing' to get an idea of how language is handled by these models at basic level. AI and machine learning are very complicated