Imagine searching the web not with clunky keywords, but through a natural, flowing conversation. That's the promise of conversational search, a rapidly evolving field that uses AI to make information access more intuitive and intelligent. This approach is transforming how we interact with search engines, moving beyond simple keyword matching to understand the nuances of human language and the context behind our questions. Conversational search systems use advanced techniques like query reformulation, which adapts your questions based on the ongoing conversation, and search clarification, where the system asks you clarifying questions to pinpoint your exact needs. This back-and-forth interaction allows for more complex and precise information retrieval, especially helpful when dealing with ambiguous or multifaceted topics. Large language models (LLMs) are playing a crucial role in this evolution, powering systems that can maintain context over multiple turns, integrate diverse information sources, and even generate human-like summaries. While there are challenges in areas like data scarcity, evaluation methods, and ensuring accuracy, the potential of conversational search is vast, promising a future where accessing information feels as natural as talking to a knowledgeable friend.