Microsoft Bing Explains Indexing for Search vs Grounded AI Answers

Microsoft Bing’s latest blog post, authored by Krishna Madhavan, Knut Risvik and Meenaz Merchant, dives into how the company’s indexing pipeline differs when it’s used for traditional web search versus for grounding AI responses.

The authors clarify that indexing for grounded AI is not a new search paradigm but a significant evolution of it, designed to provide the factual basis that AI models need to answer queries accurately. In a search context, the index is optimized for quick retrieval of the most relevant documents, ranking them by relevance and freshness. For grounding, the index is tuned to surface concise, verifiable snippets that can be cited directly in an AI answer, often leveraging structured data and knowledge graphs.

The post emphasizes that grounding commits to an answer, meaning the AI must be able to point to the exact source it used, which is a stricter requirement than typical search results. The authors also note that the underlying infrastructure shares many components, but the scoring, filtering and evidence extraction layers differ substantially. They conclude that understanding these differences is key for developers building AI-powered search or chat experiences that need to be trustworthy and explainable.