In today's column, a prompting technique known as logic-of-thought (LoT) is presented, which can enhance results from generative AI by prompting it to use logical reasoning. Although it might seem redundant to explicitly instruct AI to use logic, given that AI systems are often optimized for speed rather than deep computational reasoning, using LoT can be beneficial. This approach encourages AI to prioritize logic-based answers over quick ones. It’s essential for anyone using AI regularly to understand and apply LoT as needed. A related technique, chain-of-thought (CoT), involves guiding AI to solve problems step-by-step, often yielding better results due to a cautious and thorough approach. Integrating these strategies can significantly improve AI's problem-solving in areas requiring logical reasoning. For those dealing with logic-heavy queries, LoT involves three main steps: extracting logical propositions from a question, solving it based on these propositions, and explaining the process in plain language. A sample prompt for LoT includes asking AI to solve a logic question using these steps.

This approach is especially useful for complex problems that require careful logical analysis. Empirical research supports the effectiveness of LoT in enhancing logical reasoning tasks in AI. The technique has been tested and shown to improve performance across various logical reasoning tasks, making it a valuable tool for prompt engineering. LoT is not a universal solution but is particularly useful for logic-driven questions. Users should deploy this method when the situation calls for heightened logical reasoning. In other contexts, LoT may not be necessary, but it's a powerful tool to have available when logic takes center stage. Albert Einstein’s words remind us to use logic wisely: “Logic will get you from A to B. Imagination will take you everywhere. ”