Pinterest aims to become an inspiration-to-realization platform, understanding users' long-term goals. They introduce user journeys, defined by interests, intent, and context, to achieve this. These journeys are inferred by analyzing user interactions, moving beyond simple content recommendations. The system, built with a "lean" approach, clusters keywords from user data to identify journeys. Dynamic keyword extraction and hierarchical clustering are used to generate flexible and personalized journeys. Journey naming, expansion, ranking, and diversification are then applied to enhance user experience. A stage prediction model determines the journey's lifecycle for appropriate notifications. The output is a list of distinct user journeys with names, keywords, stage, and confidence scores. LLMs are used to evaluate journey relevance and guide system improvements. Experiments with journey-aware notifications showed significantly improved user engagement. Furthermore, Pinterest is actively leveraging LLMs to simplify and improve journey inference overall. The company is actively fine-tuning LLMs and implementing scalable batch inference for efficient execution.