An LLM is a machine that guesses the next token.

A neuron multiplies each input by a weight (how much it cares), adds the results, adds a bias (its default mood), and passes the total on.

Think of it as a judge scoring a dish: taste counts a lot, price counts against it, plus a personal generosity bonus.

Put many neurons side by side to make a layer, then chain layers, so each one turns the previous layer's numbers into slightly more useful numbers.

Think of it as an assembly line, or a team of detectives: the first desk spots edges, the next spots shapes, the last decides “that's a 7”.

Scale check 3Blue1Brown's digit reader takes 784 pixels in and gives 10 numbers out. With two hidden layers of 16 neurons it has 13,002 weights and biases.

After adding up its inputs, each neuron bends the total with a simple rule, such as ReLU: “if negative, output 0”. Bends let the network learn curves instead of only straight lines.

Think of it as a dimmer switch with a floor. Below zero the light stays off; above zero it gets brighter in step.

The loss is one number that says how wrong a guess was. Big loss means a bad guess. Zero means perfect.

Think of it as a golf score: lower is better, and the whole game is bringing it down.

Training asks, over and over: which small change to each weight would lower the loss? Backpropagation works that out. Gradient descent then nudges every weight a little that way. Repeat billions of times.

Think of it as walking downhill blindfolded in fog. You feel the slope under your feet and step downhill. The step size is the learning rate. Backpropagation is the efficient way to feel the slope for millions of dials at once, working backward from the mistake to see who is to blame.

Before the model sees text, it is chopped into tokens (common words, word pieces, or single bytes), and each token gets an ID number.

Think of it as LEGO bricks. Common words come as one big pre-built brick (“the”). Rare words are snapped together from smaller bricks (“st” + “raw” + “berry”).

Rule of thumb For English, one token is about 4 characters or 0.75 words (OpenAI docs). GPT-2's vocabulary holds 50,257 tokens.

Each token ID becomes a long list of numbers, a point in a “meaning space” where similar words sit close together.

Think of it as a map where words are addresses. “Cat” lives next door to “dog” and far from “bus”, and directions on the map carry meaning.

Scale check This map has 2 numbers per word. GPT-3 uses 12,288.

An LLM's whole job: look at the text so far, give every possible next token a probability, pick one, add it to the text, and repeat.

Think of it as phone autocomplete with a far bigger brain, or as finishing a torn movie script one word at a time.

A transformer lets every token look at the tokens before it, decide how much each one matters (attention), and blend in their information. Many layers repeat this, and all tokens are processed in parallel.

Think of it as a party. Each word holds up a query (“who describes me?”), wears a key name-tag (“I'm an adjective”), and offers a value (what it can tell you). When a query and a key match well, you listen more to that word's value. Softmax turns the match scores into percentages.

Scale check GPT-3 runs 96 attention heads per layer across 96 layers. Its MLP blocks, not attention, hold about 116 billion of its 175 billion parameters (3Blue1Brown).

The model starts with random weights and spends months reading a huge amount of text, playing “guess the next token” and being corrected by backpropagation each time.

Think of it as reading every textbook in the library. In Andrej Karpathy's textbook analogy, pretraining is the reading, fine-tuning is the worked examples, and reinforcement learning is the practice problems.

Scale check A person reading non-stop would need over 2,600 years to get through GPT-3's training text (3Blue1Brown). The result is a base model, an internet-text simulator that continues documents but is not yet a helpful assistant.

The base model is trained further on a smaller, carefully written set of example conversations (question, then ideal answer), so it learns to behave like a helpful assistant.

Think of it as an apprentice who has read the whole library and now studies worked examples from a master: “when asked this, answer like that.”

People compare pairs of answers and pick the better one. A reward model learns to predict those choices, and the LLM is then tuned to write answers the reward model scores highly.

Think of it as a cooking show. Judges taste two dishes and point to the better one, a sous-chef learns to predict the judges' taste, and the cook practices against the sous-chef thousands of times.

Why it matters In OpenAI's InstructGPT study, people preferred answers from a 1.3-billion-parameter model tuned with human feedback over answers from the 175-billion-parameter GPT-3, despite the 100× size gap.

When you chat, the frozen model produces probabilities for the next token. A sampling rule picks one, and the loop repeats until a stop token. Temperature controls how bold the pick is.

Think of it as a spice dial. Low always orders the house favorite. High tries unusual dishes. Top-p says “only consider the dishes that together make up the top slice of popularity”.