In 2025, the need for verifying real-time information in AI-generated outputs became critical after U.S. federal judges retracted opinions based on non-existent legal citations that slipped through due to the confident but incorrect nature of large language models (LLMs). Traditional evaluations lack the ability to verify real-time facts, prompting the development of tools like Promptfoo's search-rubric assertion, which combines LLMs with web searches to ensure current and accurate data in outputs. This tool is essential for applications where data rapidly changes, such as stock prices, legal citations, and software versions, allowing models to be tested against real-world information and reducing the risk of errors in dynamic and time-sensitive contexts. Promptfoo's search-rubric integrates with various AI models to provide a system where outputs are graded based on a user-defined rubric, enabling a separate "judge" model with web search capabilities to verify data accuracy. While this approach incurs additional latency and costs, it is crucial for areas where precise and current information is paramount, turning AI reliability from "trust me, it usually works" into a more formal verification process.