Chain of thought interpretability reveals the step-by-step reasoning behind AI model outputs, breaking complex decisions into human-readable logic. It helps researchers debug model behavior, boosts trust in high-stakes fields like healthcare or law, and aids developers in refining prompt engineering for more transparent, reliable AI systems.
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