What Is a Neural Network? A Plain-English Explanation

A single artificial neuron takes several numeric inputs, multiplies each by a weight, adds them up, and passes the result through a simple function that decides how strongly to "fire." On its own that's not very useful — the power comes from stacking thousands or millions of these neurons into layers, where each layer's output feeds the next.

Training is the process of adjusting every weight in the network so that, given an input, it produces the right output. That's done by comparing the network's guess to the correct answer, measuring how wrong it was, and nudging every weight slightly in the direction that reduces the error — repeated across millions of examples.

Different arrangements of the same basic building block turn out to be good at very different tasks: convolutional layers are efficient at recognizing local patterns in images, and the "attention" mechanism inside transformer architectures is what lets modern LLMs weigh which earlier words in a sentence matter most for predicting the next one.

None of this requires anyone to hand-write the actual rules ("a cat has pointy ears and whiskers") — the network derives its own internal representation of the pattern purely from being shown enough correctly-labeled examples.

A neural network only knows what was in its training data and the statistical patterns it found there — it has no built-in concept of truth, so it can produce a confident, fluent, and completely wrong answer with the same ease as a correct one (a "hallucination").

Understanding this is the whole reason techniques like retrieval-augmented generation exist: instead of trusting the network's internal, frozen knowledge for facts, you hand it real, current source text at the moment it answers, and let it do what it's actually good at — reading and synthesizing that text into a coherent response.