Navigating online health information is often overwhelming and lacks personalization for individuals. Large language models (LLMs) can improve this, but current AI tools act as passive question-answerers. An expert like a doctor actively seeks context by asking clarifying questions to provide tailored guidance. This research introduces "Wayfinding AI," an early-stage prototype based on Gemini, designed to proactively ask clarifying questions. Through user studies, this approach was found to be significantly more helpful, relevant, and tailored than a baseline AI. Participants often struggle to articulate their health concerns, making proactive questioning crucial for gathering relevant details. The Wayfinding AI uses three principles: proactive conversational guidance, best-effort answers at each turn, and transparent reasoning. Its interface separates the conversational elements from detailed information to ensure questions are not missed. User studies revealed that participants preferred the Wayfinding AI for its helpfulness, relevance, goal understanding, and tailoring. Conversations with Wayfinding AI were longer and more focused on eliciting detailed user input. This human-centered, conversational approach shows promise for future AI in health applications.