Chain of Thought
Chain of Thought is a technique that prompts large language models to generate intermediate reasoning steps before producing a final answer. This allows the model to break down complex problems into a series of logical steps, similar to how a human might solve them.
Why it matters
Chain of Thought improves the accuracy and interpretability of large language model outputs. For engineers and operators, this means more reliable predictions and a clearer understanding of how the model arrived at its conclusions, enabling better debugging and trust.
How it works
The technique involves adding specific phrasing to the prompt that encourages the model to "think step-by-step." This can be done by providing examples in the prompt that demonstrate this reasoning process or by explicitly instructing the model to output its thought process.
Auto-generated from Kapyn's news stream · updated Jun 15, 2026