Google is no longer just a list of blue links. With AI Overviews and large language models, it can generate answers directly on the results page, before the user clicks anything. This article explains the core steps of the AI Overview pipeline and what it means for SEO and LLMO.
1. From search results to direct answers
For many years, Google was mostly a list of blue links. You typed a query, got ten results, clicked one of them and the real work happened on the website. With AI Overviews, this flow has changed: Google now tries to provide a complete answer directly on the results page, before the click ever happens.
For SEOs and content teams this means one thing: you are no longer
optimising only for rankings and CTR. You are also optimising for being selected as a trusted source inside the AI Overview pipeline.
2. High level view of the AI Overview pipeline
The exact implementation is proprietary, but we can describe a simplified version of the pipeline based on what we observe and on how large language models work in general.
Step 1 – Decide if the query is a good fit for AI Overview
First, Google classifies the query:
Is it informational, navigational, transactional or mixed?
Does the user expect a short direct answer or a structured overview?
Is the topic sensitive (health, finance, safety, news)?
Only certain types of queries are eligible for AI Overviews. For some topics, Google will prefer classic results or specialised modules (for example flights, hotels, maps).
Step 2 – Fetch candidate pages from the index
If the query is a good fit,
Google AI Overview Pipeline fetches a set of candidate URLs from its index. This step reuses many of the classic ranking signals:
textual relevance to the query,
site quality and authority,
freshness and update frequency,
technical health and mobile usability.
In other words, good SEO is still required. If your pages are not even in the candidate set, they cannot become sources for AI Overviews.