To improve an LLM’s answers for complex problems that need detailed, step-by-step reasoning, which prompt engineering technique should be used?

Chain-of-thought prompting is the most effective technique for complex problems requiring detailed, step-by-step reasoning. It encourages the LLM to generate a series of intermediate reasoning steps before providing the final answer, mimicking human thought processes. This makes the model's reasoning explicit and often leads to more accurate results for intricate tasks. Few-shot prompting provides a few examples to guide the model, which can help but doesn't explicitly force step-by-step reasoning. Zero-shot prompting provides no examples, relying solely on the model's pre-trained knowledge, which is less effective for complex reasoning. Directional stimulus prompting is not a standard or widely recognized prompt engineering technique.