Generative Engine Optimization: What Research Says

Generative search is no longer a lab demo. It is a daily workflow for millions of users who ask a question and get a synthesized answer with citations (or sometimes, no clear citation at all). That shift changes the incentive for publishers and marketers: ranking a page is still valuable, but being used inside the answer is increasingly the difference between “seen” and “skipped.”

One of the most cited academic attempts to put this on solid ground is the KDD 2024 paper GEO: Generative Engine Optimization by Aggarwal et al. It does something rare in marketing-adjacent research: it proposes definitions, metrics, a benchmark, and then tests concrete content changes at scale.

What the paper calls “generative engine optimization”

The paper defines generative engines as systems that retrieve documents and then use large language models (LLMs) to produce a single response grounded in multiple sources (think Perplexity, Bing Chat style experiences, and other “answer-first” interfaces).

In classic SEO, visibility is largely modeled as ranking and clicks from a list of blue links. In generative engines, visibility is more nuanced:

Your content may appear as an inline citation supporting one sentence.
Your content may contribute to multiple parts of the answer.
Your source might be cited early (high attention) or late (low attention).

The paper’s key framing is that generative engine optimization is about improving source visibility inside generated answers, not only improving rankings in a traditional results list.

If you want a practical comparison of GEO vs classic SEO from an operator perspective, this complements the research well: GEO vs SEO: key differences and similarities.

How the research measures “visibility”

A major contribution of the paper is admitting that “rank” does not translate cleanly to AI answers, then proposing measurable proxies.

Impression metrics

They introduce objective metrics (based on text placement) and a subjective metric (LLM-based judging) to reflect how prominent a citation is.

Metric	What it measures	Why it matters for GEO
Word count impression	Share of words in the answer attributed to a source	If more of the answer is supported by you, you are more “present”
Position-adjusted word count	Word count weighted by where the cited sentences appear	Early citations tend to get more attention than late ones
Subjective impression (LLM-based)	Multi-factor score (relevance, influence, uniqueness, diversity, click likelihood, etc.)	Captures “felt” prominence, not just raw length

This is an important mindset shift for SEO teams: GEO performance is closer to share of answer than position in a list.

For a broader measurement view that bridges classic SEO and LLM visibility, see LLMO explained.

A simple illustration of a generative engine workflow: user question on the left, a retrieval step pulling several web sources in the middle, and an LLM-generated answer on the right with inline citations highlighting where sources are referenced.

What they tested (and how)

The paper proposes a black-box optimization approach: you do not control the generative engine, but you can change your page.

The benchmark: GEO-bench

To evaluate methods systematically, they curate GEO-bench, a 10,000-query benchmark spanning multiple datasets and query types (informational, navigational, transactional). Each query is paired with the top web results used as sources.

The generative engine setup

In their primary experiments they:

Retrieve top sources from Google (top 5).
Generate an answer grounded in those sources using an LLM.
Measure how often, how prominently, and how influential the optimized page becomes.

They also validate results on a real deployed generative engine: Perplexity.ai (by uploading source text so the engine uses provided sources).

The meta takeaway for practitioners is that the research tries to reflect a realistic pipeline: retrieval first, then synthesis.

What worked (research-backed)

The headline result: the paper reports that GEO methods can increase visibility by up to ~40% in generative engine responses, depending on metric and domain.

Even more actionable is which edits helped.

High-performing methods

Across the benchmark, the strongest lifts came from content changes that increase verifiability and quotability:

Quotation addition
Statistics addition
Cite sources (adding credible citations in the content)
Fluency optimization (making text clearer and smoother)

A key table in the paper shows that (in their setup) Quotation Addition produced the highest position-adjusted visibility score, and Statistics Addition also performed strongly. They summarize this as best methods improving visibility by about 41% on position-adjusted word count and 28% on subjective impression (relative to baseline).

What did not work well

The most “SEO-looking” tactic underperformed:

Keyword stuffing (adding more query keywords) showed little to no improvement, and in some cases was worse.

That is a useful corrective for teams porting old playbooks into AI answers: generative engines appear to reward evidence density and clarity more than brute keyword frequency.

GEO method (from the paper)	Research signal	What it implies for your pages
Quotation addition	Very strong lift	Add attributable quotes from credible sources, especially for explanations and narratives
Statistics addition	Strong lift	Replace vague claims with numbers, ranges, and measurable outcomes where truthful
Cite sources	Strong lift	Support key statements with reputable references, improving “grounding”
Fluency optimization	Strong lift	Write for skimmability and clean synthesis (short sentences, clear structure)
Easy-to-understand	Moderate lift	Reduce complexity where possible, without oversimplifying
Authoritative tone	Mixed	Style helps in some domains, but evidence beats tone
Keyword stuffing	Weak to negative	Avoid chasing “SEO tricks” that reduce quality

If you want a tactical playbook specifically focused on getting cited by LLMs (beyond this research summary), pair this with How to make content cited by ChatGPT.

Domain matters

A subtle but important finding is that there is no universal “best” GEO tactic. The paper shows that different methods win in different query categories.

Examples they report:

Authoritative tone tends to help more for debate style and some history/science queries.
Cite sources helps more for statement-of-fact queries.
Statistics addition is especially effective in domains like law/government and opinionated questions where evidence strengthens claims.

For content teams, this suggests a shift from “one global template” to method selection by cluster, similar to how you already adapt content formats by SERP intent.

One of the most interesting findings: GEO can help smaller sites

The paper also analyzes how GEO affects sources that were originally lower-ranked in classic search results.

Their reported pattern is that lower-ranked pages can benefit disproportionately from GEO tactics, because generative engines are not purely link-authority driven in how they assemble answers. In their experiments, adding citations, quotes, and stats could shift visibility toward a previously less-prominent source.

This does not mean backlinks stopped mattering, it means there may be a second battleground: being the most useful building block for an answer.

What this means for content strategy in 2026

Research is not a turnkey SOP, but it does suggest a practical north star: make your content easy to trust, easy to quote, and easy to attribute.

Here is how that translates into editorial decisions that usually help both GEO and SEO:

Write “citation-ready” sentences. Make key claims standalone, specific, and supported.
Upgrade vague claims into measurable ones. Use statistics only when you can verify them.
Add credible references. Not to stuff outbound links, but to create a traceable chain of evidence.
Use clean structure. Headings that match questions, short paragraphs, descriptive tables.
Improve fluency before you add volume. Generative engines summarize, they punish messy writing.

For teams operating in a zero-click environment, this research also fits neatly with “visibility-first” planning: Zero-click search strategy for Google AI Overviews.

A content checklist visual showing four GEO levers: add statistics, add quotations, cite sources, and improve fluency, each connected to increased citation visibility in AI answers.

Where the research is limited

The authors are explicit that GEO methods may change as generative engines evolve. A few constraints to keep in mind when applying the findings:

Black-box systems drift. Models, prompts, and retrieval stacks change.
Their benchmark is large, but not “the internet.” GEO-bench covers many domains, not every niche.
Metrics are proxies. Position-adjusted word count is intuitive, but it is not the same as clicks or revenue.

In other words, treat the paper as strong directional guidance, then validate with your own monitoring.

If you are scaling AI-assisted publishing, you will also want to pair GEO tactics with trust signals and review process, see E-E-A-T for automated blogs.

How to operationalize GEO without adding manual work

Most teams do not fail at GEO because they do not know what to do, they fail because implementing it across dozens or hundreds of pages is operationally expensive.

BlogSEO is built for exactly that kind of execution problem: it automatically generates and publishes SEO-optimized articles, and it supports workflows that matter for GEO-adjacent work such as website structure analysis, keyword research, competitor monitoring, brand voice matching, internal linking automation, and auto-scheduling.

If your goal is to turn research-backed GEO patterns into a repeatable publishing system, start here:

Try BlogSEO: blogseo.io
Or book a demo: Schedule a call

FAQ

What is generative engine optimization? Generative engine optimization is the practice of improving how often and how prominently your content is cited or used inside AI-generated answers from generative search engines.

Does keyword stuffing help with GEO? The research in the GEO paper found keyword stuffing performed poorly compared to evidence-based edits like adding statistics, quotations, and citations.

What GEO tactics had the biggest impact in the study? Quotation addition and statistics addition were among the strongest performers, and adding citations plus improving fluency also increased visibility significantly.

How do you measure GEO performance on your own site? Track citations and “share of answer” across target queries (for example, AI Overview citations and Perplexity citations), then connect those exposures to assisted conversions and branded search lift.

Will GEO replace SEO? No. GEO changes the surface area where content is discovered, but strong technical SEO, crawlability, and authority still influence whether your pages are retrieved in the first place.

Build a GEO-ready content engine

If you want to apply what research says about generative engine optimization without turning it into a manual editorial tax, use an automation layer that can publish consistently, maintain internal linking, and keep output aligned with your brand voice.

Start a 3-day free trial on BlogSEO or book a demo to see how an auto-publishing workflow can support both classic SEO and GEO visibility.