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Sally has 1 sister. Here's the breakdown: Each brother has 2 sisters. Sally is one of those sisters (since she's their sister too). That means there is 1 other sister besides Sally. So Sally has 1 sister.

Sally has 1 sister. Here is the breakdown: If each brother has 2 sisters, that means there are 2 girls in the family total. Since Sally is one of those girls, the other girl is her 1 sister.

Sally has 1 sister. Here's the reasoning: Sally's 3 brothers each have 2 sisters. Sally herself is one of those sisters. So there must be exactly one other girl in the family. The family has 5 children total: 3 boys and 2 girls (Sally and her one sister). Each brother sees the same 2 sisters — Sally and her sister.

The Experienced Software Engineer To understand how an LLM learns, discard the idea of a traditional database or rules engine; instead, think of training as building a massively distributed, lossy compression algorithm.

To the Experienced Software Engineer At its core, training a Large Language Model is essentially a massive, distributed, continuous optimization job. Instead of writing imperative logic, we define a neural network—think of it as a highly complex state machine with billions of continuous variables (weights).

The Skeptical Software Engineer Think of an LLM as the world's most aggressive lossy compression problem. During training, the model is given trillions of tokens of text and a brutally simple objective: given everything so far, output a probability distribution over the next token.