Large language models (LLMs) have shown remarkable progress in various tasks, but multi-hop question answering (MHQA) remains a significant challenge. MHQA requires models to synthesize information from multiple sources to answer complex, indirect questions—a skill that mimics human reasoning. Think of it like a detective solving a case: they need to piece together clues from various witnesses and evidence to arrive at the truth. Current LLMs often struggle with this process, succumbing to errors like hallucination (making things up), error propagation (letting early mistakes snowball), and losing track of the original question amidst lengthy reasoning chains. Researchers are constantly exploring new ways to improve LLMs’ multi-hop reasoning abilities. A novel approach called Self-Guiding Finite State Machine prompting (SG-FSM) aims to enhance this skill by breaking complex questions into smaller, manageable sub-questions. Imagine teaching a child a complicated math problem: you would guide them step-by-step, ensuring they understand each part before moving on. SG-FSM adopts this principle, working like a well-defined roadmap, processing one sub-question at a time and dynamically adjusting its course based on the current context. This controlled, iterative process reduces errors and helps the LLM stay focused on the ultimate goal. Experiments on challenging MHQA datasets like Musique, which requires reasoning over longer texts and multiple hops, show promising results. SG-FSM significantly outperforms traditional methods, demonstrating its potential to unlock more robust, human-like reasoning in AI. However, challenges remain. The multi-turn dialogue nature of SG-FSM can be demanding for smaller language models, and the effectiveness of the method relies heavily on the underlying LLM's ability to follow instructions. Despite these challenges, SG-FSM represents an important step towards enhancing AI's multi-hop reasoning abilities. As research progresses, we can expect even more sophisticated methods to emerge, bridging the gap between AI and human reasoning, opening doors to even more complex and nuanced applications in areas like advanced information retrieval, complex problem-solving, and more human-like conversational AI.