Microsoft Bing Grounds AI Answers with New Indexing Framework

Microsoft’s Bing team released a framework that distinguishes indexing for traditional search from indexing that grounds AI answers. The framework identifies five measurement areas where the two systems diverge: factual fidelity, source attribution quality, freshness, coverage of high‑value facts, and contradictions. In grounding, factual fidelity is paramount because a stale or incorrect fact can produce a misleading answer, and the system must abstain when evidence is missing or conflicting. Source attribution is a core signal, and the index must provide clear, reliable citations for every grounded response. Freshness is treated as a ranking problem in search but a critical quality metric in grounding, while coverage of high‑value facts requires the index to ensure that the specific facts users are likely to ask about are available. Contradictory sources cannot be surfaced side‑by‑side; instead, the system must avoid arbitrating between them. Microsoft also introduced design choices such as abstention—valid when support is lacking—and iterative retrieval, which allows the AI to refine queries and combine evidence from multiple sources. Recent tooling updates include the AI Performance dashboard in Bing Webmaster Tools, rewritten guidelines with GEO optimization, and upcoming features like Citation Share and grounding query intent labels.