Smart search, often called semantic search, improves traditional keyword matching by understanding the context, meaning, and intent behind user queries. Instead of just looking for exact word matches, it analyzes natural language, synonyms, relationships between words, and even user context. This allows the system to grasp what the user actually means, not just the specific terms they typed, resulting in more relevant results even for complex or ambiguously phrased requests.

A key use is in e-commerce platforms; searching for "comfortable running shoes for flat feet" would return relevant products based on features, reviews mentioning comfort/arch support, not just items containing all those words. Customer support chatbots also heavily rely on semantic search to understand varied user questions like "My order hasn't arrived" or "Where's my package?" and connect them to the correct resolution path, regardless of the exact phrasing.

The main advantage is significantly improved search relevance and user experience. Limitations include the requirement for substantial quality training data, greater computational complexity, and potential challenges interpreting highly niche or ambiguous queries. Ethical considerations involve ensuring personalization doesn't become invasive. Future development focuses on integrating generative AI for truly conversational search interactions, expanding its capabilities beyond simple understanding.