Chain-of-Thought directive

The Chain-of-Thought (CoT) directive is a prompting technique used to guide an AI model to solve complex problems by explicitly breaking down the reasoning process into intermediate steps. Instead of asking the AI to generate a direct answer, the CoT directive encourages it to “think out loud,” emulating a logical, step-by-step approach to arrive at a solution.

Chain-of-Thought Prompting Works because:

Encourages Logical Progression: By laying out intermediate steps, CoT prevents the AI from jumping to conclusions or skipping crucial reasoning steps.

Reduces Errors: Breaking down the problem helps reduce errors caused by conflating multiple concepts or skipping key steps.

Increases Transparency: CoT makes the reasoning process visible to the user, allowing them to understand how the AI arrived at its conclusion.

Leverages Latent Knowledge: Language models have vast latent knowledge stored in their parameters. CoT enables them to connect these dots more effectively by prompting them to articulate reasoning.

Improves Performance on Complex Tasks: CoT has been shown to significantly enhance the performance of models on tasks that require multi-step reasoning, such as math problems, logical puzzles, and text-based deduction.