Hu Xiu·2025-10-13 06:13Core Insights - The article discusses the limitations of traditional search engines like Google, which primarily rely on keyword matching without understanding user intent, contrasting this with the capabilities of AI-driven search tools like Websets that aim to comprehend complex queries [2][4][24]. Group 1: Search Engine Limitations - Traditional search engines, such as Google, often fail to grasp the nuances of user queries, leading to irrelevant results [2][4]. - Google provides a plethora of links related to popular content rather than directly answering subjective questions, exemplified by the query about "the most beautiful woman" [13][14]. - The reliance on keyword indexing means that Google excels in factual queries but struggles with complex, multi-faceted tasks [22][24]. Group 2: Websets Capabilities - Websets is designed to handle structured queries and can process complex tasks that traditional search engines cannot, such as finding professionals with specific experiences [4][15]. - It utilizes a deep learning model to create a "semantic fingerprint" of web content, allowing it to match user queries with relevant data more effectively [28][30]. - The tool provides structured outputs, such as candidate lists for specific roles, demonstrating its ability to analyze and filter information based on user-defined criteria [27][30]. Group 3: Data Source Limitations - Websets relies heavily on LinkedIn for sourcing information, which may lead to biases and limitations in its results, particularly for experts not well-represented on that platform [40][41]. - The effectiveness of Websets diminishes in markets like China, where alternative professional networking platforms are more prevalent [41][42]. Group 4: Semantic Search Technology - Websets employs "embedding" technology, which compresses complex information into numerical representations, allowing for nuanced understanding of queries [24][46]. - This method, while effective for grasping overarching themes, may lose specific details during the compression process, highlighting a potential drawback in retrieving precise information [46][48]. Group 5: Market Context and Future Implications - The emergence of AI-driven search tools like Websets indicates a shift in search technology, suggesting a future where search engines may evolve to better understand user intent [50]. - The article emphasizes the importance of recognizing the trade-offs between convenience and the depth of information retrieval in modern search practices [62][63].