Self-Ask Prompting

Self-Ask Prompting is a reasoning technique that instructs AI models to break down complex problems by asking themselves intermediate questions and answering them sequentially before providing the final response. This method enhances logical reasoning by creating a structured thought process where the model explicitly identifies sub-questions, retrieves relevant information, and builds upon previous answers to reach comprehensive conclusions. Self-ask prompting proves particularly effective for multi-step reasoning tasks, research queries, and analytical problems that require systematic investigation. The technique reduces hallucination rates and improves accuracy by forcing models to validate their reasoning at each step. Advanced implementations combine self-ask prompting with retrieval-augmented generation, enabling models to query external knowledge sources when encountering questions beyond their training data, resulting in more reliable and verifiable outputs.