How Google Builds Answers Before the Click (AI Overview Pipeline)

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 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.

Step 3 – Extract the useful pieces of information

From the candidate pages, Google needs to extract the pieces that can be used to answer the query: definitions, steps, comparisons, parameters, pros and cons, and so on. This is where LLMO becomes highly relevant.

Pages that are:

• clearly structured with headings and subheadings,
• rich in facts, numbers and concrete statements,
• supported by FAQ and HowTo sections,
• marked up with schema (Article, FAQPage, HowTo, Product, Organization),

are simply easier for models to read and to turn into reliable building blocks for the final answer.

Step 4 – Generate the AI Overview answer

Once enough information is extracted, a large language model is used to generate the visible AI Overview block. The model:

• combines facts from multiple sources,
• simplifies complex explanations,
• organises the output into paragraphs, lists or steps,
• tries to avoid contradictions and unsafe recommendations.

The goal is not to replace expert content, but to provide a starting summary that helps the user understand the topic faster.

Step 5 – Attach citations to source pages

The final AI Overview includes links to the pages that influenced the answer. Being cited here is extremely valuable:

• you gain visibility even if you are not the top organic result,
• you become the natural next step for users who want to go deeper,
• your brand is associated with expert-level information.

3. What signals are important for AI Overview selection

Based on what we see across projects, the following elements strongly increase the chance that your pages are picked up by the pipeline:

• Structured content – clear H2/H3 sections for each subtopic.
• High information density – concrete facts, examples, parameters.
• FAQ and HowTo blocks – direct answers to common questions.
• Rich schema markup – at minimum Article + Organization, ideally FAQPage/HowTo/Product.
• EEAT signals – visible author, organisation, policies, real-world contact details.

4. How to make your content AI Overview ready

You cannot force Google to show an AI Overview. What you can do is make your content a natural candidate for being used as a source.

1. Identify queries where users clearly expect an explanation or a step-by-step guide.
2. Create or update pages so that they fully answer these queries on a single URL.
3. Add FAQ sections with question–answer pairs in plain language.
4. Add HowTo sections for processes and workflows, with clear steps.
5. Implement schema markup (Article, FAQPage, HowTo, Product, Organization).
6. Strengthen EEAT by showing who you are, why you are credible and how to contact you.

5. Frequently asked questions (FAQ)

Does every query trigger an AI Overview?

No. AI Overviews are shown only for selected queries where an AI-generated summary can add value. For other queries Google still relies on classic results, vertical search modules or ads.

Will AI Overviews reduce my clicks?

For simple factual queries, some clicks may move from websites to direct answers. For complex decisions and high intent topics, being cited inside an AI Overview can actually increase the quality of traffic that reaches your site.

How does LLMO help in this context?

LLMO focuses on making your content easy to extract, understand and reuse for large language models. It builds on top of SEO: you still need a technically healthy website, but you also design content specifically for AI systems, not only for human readers.