AI models are notoriously likened to black boxes, meaning the humans who build them can’t look inside to see how they transform mountains of training data into lines of code, sonnets, or whatever else they’re asked to generate. Not completely, anyway. A subfield called interpretability research has blossomed in recent years, aimed at shining various lights on how AI models “think.” One of the brightest lights is called chain-of-thought reasoning, or CoT. Think of it like a recorded transcript of the steps models take while working through problems—like a student showing their work on a test. It’s widely regarded as
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