Robby Stein from Google discussed how AI Mode utilizes established search quality signals and highlighted five factors used to assess content during a recent interview. He emphasized that AI Mode builds on Google’s extensive experience in search to determine the helpfulness of content, incorporating metrics such as clicks. Stein also addressed concerns about potential inaccuracies in AI responses, assuring that the system is grounded in Google’s decades of expertise in evaluating quality. The interviewer raised the issue of maintaining consistency and high quality in Google searches despite occasional inaccuracies in AI-generated results.

Stein reassured that AI Mode draws on the foundation established by Google’s classic search systems rather than starting anew, managing the risk of inaccuracies by aligning AI answers with long-standing signals of relevance, trustworthiness, and usefulness. The discussion further delved into the quality signals employed by Google within AI Mode, with Stein noting the similarity in how quality is determined compared to classic search methods.

When asked about ensuring the best user experience amid evolving search results, Stein explained that a variety of factors are considered, including assessing helpfulness through real-time feedback and user engagement patterns. Success is not merely gauged by increased usage but by users’ satisfaction and utility derived from search results. This approach maintains continuity with classic search principles while adapting to dynamic interfaces.

Regarding SEO best practices for ranking AI-generated content, Stein outlined five key factors used to evaluate content quality and relevance. These factors include direct responses to user queries, content quality, loading speed, originality, and source citations. Content that meets these criteria effectively addresses user needs and is valued by users is more likely to rank highly in both traditional search and AI-generated results.