Chain-of-Thought Prompting: Techniques and When To Use Them

Large language models often struggle to provide direct answers to complex queries. Chain-of-thought (CoT) prompting addresses this issue by enhancing logical transparency, aiding teams in debugging incorrect outputs and verifying the model's conclusions. CoT prompting elicits a sequence of intermediate reasoning steps that the model uses to arrive at its response. A study published in Frontiers Media SA revealed that CoT had the lowest hallucination rate at 18.1%, compared to 34.5% for other prompting methods, demonstrating its effectiveness in improving answer accuracy.