Core Viewpoint - Google is returning to its core business of search by introducing the Gemini API's URL Context feature, which allows AI to "see" web content like a human [1]. Group 1: URL Context Functionality - The URL Context feature enables the Gemini model to access and process content from URLs, including web pages, PDFs, and images, with a content limit of up to 34MB [1][5]. - Unlike traditional methods where AI reads only summaries or parts of a webpage, URL Context allows for deep and complete document parsing, understanding the entire structure and content [5][6]. - The feature supports various file formats, including PDF, PNG, JPEG, HTML, JSON, and CSV, enhancing its versatility [7]. Group 2: Comparison with RAG - URL Context Grounding is seen as a significant advancement over the traditional Retrieval-Augmented Generation (RAG) approach, which involves multiple complex steps such as content extraction, chunking, vectorization, and storage [11][12]. - The new method simplifies the process, allowing developers to achieve accurate results with minimal coding, eliminating the need for extensive data processing pipelines [13][14]. - URL Context can accurately extract specific data from documents, such as financial figures from a PDF, which would be impossible with just summaries [14]. Group 3: Operational Mechanism - The URL Context operates on a two-step retrieval process to balance speed, cost, and access to the latest data, first attempting to retrieve content from an internal index cache [25]. - If the URL is not cached, it performs real-time scraping to obtain the content [25]. - The pricing model is straightforward, charging based on the number of tokens processed from the content, encouraging developers to provide precise information sources [27]. Group 4: Limitations and Industry Trends - URL Context has limitations, such as being unable to access content behind paywalls, specialized tools like YouTube videos, and having a maximum capacity of processing 20 URLs at once [29]. - The emergence of URL Context indicates a trend where foundational models are increasingly integrating external capabilities, reducing the complexity previously handled by application developers [27].