What is Chain of Thought
Chain of Thought (CoT) is a prompting technique for AI language models where the model is guided to articulate its reasoning step-by-step before arriving at a final answer. It enables the model to explicitly display its internal thought process.
Instead of jumping directly to a conclusion, CoT encourages the model to break down a problem into intermediate, logical steps. This approach significantly improves the model's performance on complex reasoning tasks like arithmetic, commonsense reasoning, and symbolic manipulation. Its effectiveness arises from mimicking human-like reasoning, allowing the model to solve problems that require multi-step logic. It is particularly valuable when transparency in the reasoning is desired or necessary to validate the answer. Proper application requires crafting prompts that explicitly ask for step-by-step reasoning.
This technique greatly enhances the model's accuracy and reliability on challenging tasks by reducing errors from overlooked steps or faulty logic. It allows users to follow and audit the reasoning path, identify potential flaws, and provides insights into how the model interprets problems. Its primary value lies in enabling complex problem-solving with greater transparency and verifiability.
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