Understanding the pipeline behind an AI answer removes most of the guesswork from trying to appear in one. It also explains several things that otherwise look arbitrary, such as why the same question produces different sources on different platforms, or why a page that ranks first on Google is sometimes ignored entirely.
This article walks through the process in plain English. No engineering background required, though a few technical terms are worth introducing properly because they get used loosely elsewhere.
Three Architectures, Not One
The first correction to make is that there is no single mechanism behind AI answers. At least three distinct approaches are in use, and they behave differently.
Answering From the Model Alone
A language model can answer from what it absorbed during training. There is no retrieval step and no live document, so there is usually no citation either. This is why a model can give a confident answer about a subject with no sources attached, and why that answer may reflect a world several months out of date.
For publishers, this mode is largely unreachable. You cannot optimise your way into training data on any sensible timescale, and it is not where citations come from.
Grounded Retrieval
This is where citations live. The system searches for relevant documents, feeds them to the model, and asks it to answer using that material. The technique is commonly called retrieval augmented generation, or RAG. Google describes it as grounding and states plainly that it relies on core Search ranking systems to retrieve relevant, current pages from the Search index, after which its systems review the specific information from those pages to generate a response with clickable supporting links.
Google’s AI Overviews and AI Mode, ChatGPT’s search behaviour and Perplexity all operate broadly in this family, though their retrieval layers differ substantially.
Agentic Browsing
A newer mode, where an agent visits pages directly to complete a task rather than to summarise them. It may render a page, inspect its structure and interpret its accessibility tree in order to compare specifications or complete a booking. Google now points site owners towards agent friendly practices and notes that protocols are emerging to let search agents do more.
This produces a different kind of visibility. The question stops being whether your content is quotable and becomes whether your site is navigable by software.
Describing RAG as the explanation for all AI search is a common error. It explains most citations. It does not explain everything you will see.
Traditional Search Versus Generative Search
The mechanical difference is where the synthesis happens.
A traditional engine matches a query to documents, ranks them and returns the list. Synthesis is the user’s job: they open several tabs, compare, and draw a conclusion.
A generative system performs the retrieval and the synthesis, returning a conclusion with the sources attached. The retrieval half is broadly familiar. The synthesis half is new, and it is where the interesting behaviour comes from.
Stage One: Query Understanding and Expansion
The system first has to work out what is actually being asked. Prompts tend to be longer and more contextual than typed queries, often containing constraints, follow up references and implied conditions.
Many systems then expand the query. Google documents this as query fan out, generating a set of concurrent related searches to gather more information than the original phrasing would surface. Its published example expands a question about a weed filled lawn into searches covering herbicides, chemical free removal and prevention.
Bing’s reporting exposes something similar from the publisher side. Its AI performance data reports grounding queries, described as the key phrases the AI used when retrieving content that was subsequently cited, which is effectively a window onto the expanded query set rather than the user’s original wording.
The implication for publishers is significant. The query that led to your citation may bear little resemblance to anything in your keyword research.
Stage Two: Retrieval and Candidate Documents
Each expanded query runs against an index, producing a candidate pool of documents. This pool is small, typically dozens rather than thousands.
Which index depends on the platform, and this is the most consequential difference between them. Google retrieves from its own Search index, with the documented requirement that a page be indexed and eligible to appear with a snippet. ChatGPT search draws on external search providers alongside OpenAI’s own crawler, OAI-SearchBot, which must not be blocked in robots.txt for content to be included in summaries and snippets. Perplexity operates its own retrieval over a real time search of the web.
Everything downstream operates only on this pool. A document outside it has no path into the answer regardless of quality, which is why access and indexing checks come before any content work.
Stage Three: Ranking and Relevance
The candidate pool gets ordered. On Google, this uses the core ranking systems that power Search, which is the basis for its position that ordinary SEO remains the relevant discipline. OpenAI states that ChatGPT ranks search results using multiple factors intended to surface relevant, reliable information, and that placement is not guaranteed. It does not publish the factors.
This is the point where honest explanation has to stop. There is no published, universal ranking mechanism for AI citations, and any article presenting one is describing an inference.
Stage Four: Context Selection
Here is the stage most explanations skip, and it is the one that best explains why highly ranked pages sometimes go uncited.
The model cannot read every retrieved document in full. There is a finite context window, and the system has to decide which material to pass through. That usually means selecting passages rather than whole pages: the paragraphs that appear most likely to support an answer to the specific question.
Two consequences follow. First, the competitive unit is the passage, not the page. Second, extractability matters mechanically rather than aesthetically. If the relevant fact is spread across four paragraphs and qualified three times, the passage carrying it is less useful than a competitor’s single clear sentence stating the same thing.
This is a genuine reason to write clearly. It is not a reason to chop content into fragments, and Google states explicitly that no such chunking is required for its systems to understand a page containing multiple topics.
Stage Five: Answer Generation
The model now writes an answer using the selected context. It is instructed, in various ways, to base the response on the supplied material rather than on its own recall.
Notice what is being produced. The system is not summarising your page. It is composing a new text that draws on several sources, resolving conflicts between them and deciding what to include. Your content is an input to an argument you do not control.
This explains a common frustration. A brand may be described in an AI answer using framing it would never choose, assembled from a review site, a forum thread and its own product page. The answer is not a quotation. It is a synthesis.
Stage Six: Attribution and Citation Placement
Finally, sources are attached. How this works varies visibly across platforms.
Perplexity places numbered footnotes against specific claims and distinguishes in its interface between sources it selected and sources it reviewed. Google’s AI features attach links to supporting pages and have been adding more prominent in line linking over time. ChatGPT presents inline citations with a sources panel.
Two things are worth understanding about attribution. It is generally claim level rather than answer level, so a source appears because it supported a particular statement. And attribution is applied after generation, which is why it occasionally goes wrong in the ways described below.
Why Different Systems Cite Differently
Given the pipeline, the divergence is unsurprising. Different indexes produce different candidate pools. Different ranking systems order them differently. Different context limits select different passages. Different generation and attribution behaviours produce different citation patterns.
There is also non determinism. The same prompt can return different sources on consecutive runs, which has a direct methodological consequence: single prompt checks tell you almost nothing, and any assessment of AI visibility needs repetition across time to mean anything. This is covered in our guide to measuring AI search visibility.
Where the Process Breaks Down
Hallucination
Grounding reduces fabrication but does not eliminate it. A model can produce a statement not supported by the retrieved material, or blend two sources into a claim neither made. Citations do not solve this, and can make it worse by lending unearned confidence.
Misattribution
A citation can point to a source that does not actually contain the claim. This happens when attribution is applied to a generated sentence rather than derived from a specific passage. Publishers occasionally find themselves credited with figures they never published.
Stale Information
Retrieval systems reflect what has been crawled. If your prices changed last week and the index holds last month’s page, the answer will be wrong and will cite you as the source of the error. Recrawl frequency varies by site, which is a practical argument for keeping volatile information on pages that are updated and crawled regularly.
Source Reliability
Retrieval quality caps answer quality. If the candidate pool for a niche question contains mostly weak material, the answer will reflect that. This creates an opening for businesses with genuine expertise in under documented areas, where being the best available source is achievable rather than aspirational.
Uneven Coverage
Some questions have thin retrievable material because the relevant knowledge sits in people’s heads, in paid tools, or behind logins. Systems still answer them, using whatever exists. The gap between what is known and what is retrievable is where a lot of AI inaccuracy originates.
What This Means for Publishers
Reading the pipeline backwards produces a clear order of priorities.
Be in the pool. Indexing, crawler access and snippet eligibility determine whether anything else matters. This is technical, checkable, and where most failures occur.
Be selectable at passage level. Write so that important facts sit in clear, self contained statements rather than being distributed across qualified paragraphs.
Be corroborated. Claims supported elsewhere are safer for a system assembling an answer than claims that exist only on your site.
Be current where currency matters. Volatile information needs to live somewhere that gets recrawled.
Be navigable. As agentic browsing grows, whether software can move through your site becomes a visibility question rather than purely an accessibility one.
None of this is exotic. It is what you would do if your goal were to be genuinely useful to someone trying to answer a question accurately, which is, in effect, what the system is attempting.
Frequently Asked Questions
Do all AI search engines use RAG?
No. Retrieval augmented generation explains most cited answers, including Google’s grounded generative features, but models also answer from training data without retrieval, and agentic systems browse pages directly. Treating RAG as the universal explanation leads to incorrect assumptions about how visibility works.
Why does the same question give me different sources each time?
Generation is non deterministic and retrieval results shift as indexes update. Query expansion may also produce a different set of sub queries on each run. This is why prompt monitoring requires repeated sampling rather than one off checks.
Can I stop AI systems citing my content?
Partly. Blocking specific crawlers removes you from the corresponding search features, and noindex and nosnippet directives limit what can be shown. Bear in mind the trade off: blocking OpenAI’s search crawler removes you from ChatGPT search results entirely.
What is the difference between grounding and training?
Grounding means retrieving live documents to inform a specific answer, which is where citations come from. Training means learning patterns from data during model development, which produces no citations and cannot be influenced through optimisation.
If an AI answer misquotes my content, what can I do?
There is no formal correction route on most platforms. The practical options are to make the correct information clearer and more prominent on your own pages, ensure it is corroborated elsewhere, and check whether an outdated cached version is the actual source of the problem.
Published by BrandingX UK.
