Watch: Web Search Grounding
The process where an AI model runs a live web search, reads the results, and weaves some of those pages into its answer.
Transcript
When an AI model answers a question by searching the live web instead of relying on its memory, it is using a process called web search grounding. It is how platforms like Google, OpenAI, and Anthropic pull fresh sources into their answers.
While every platform uses this same basic funnel, they all handle the data very differently under the hood. The process starts when a search retrieves a batch of web pages. The AI then filters these down to the pages with readable content, and finally, selects a small subset of those pages to actually cite in the final answer.
The gap between how many pages are retrieved and how many are actually cited reveals the unique personality of each search engine. In a recent head-to-head test, the differences were stark. Google retrieved seven pages and cited every single one of them. OpenAI cast a massive net, retrieving nearly forty pages, but ultimately cited only two. Anthropic took a middle ground, retrieving fourteen pages and citing nine.
For creators and publishers, this funnel is the entire game. To get your content seen by users, your page has to survive every step of the process. It must be retrieved, it must be easily readable by the AI, and it must prove valuable enough to earn that final citation.