GEO's Cousins: Making Sense of SEO, AEO, GEO, and LLMO

Series

• GEO: Generative Engine Optimization - Explained
• GEO: Generative Engine Optimization - Applied
• GEO’s Cousins: Making Sense of SEO, AEO, GEO, and LLMO (this post)

If you read the first two posts in this series, you already know what GEO is and how to implement it in an Astro project (you can search for other examples). Good. Keep that mental model — you’re going to need it, because GEO isn’t the only new acronym around.

Scroll through any good tech-based marketing blog right now and you’ll run into AEO, LLMO, AIO, and a handful of vendor-invented variants, all claiming to be the thing that replaces SEO. The thing is that they didn’t replace it. They’re overlapping lenses on the same underlying shift — content discovery expanding from a ranked list of links to a mix of ranked links, extracted answers, generative summaries, and AI-mediated conversations.

One caveat before we go further: none of these terms — AEO, GEO, LLMO, AIO — are standardized industry disciplines with fixed definitions. They emerged from a mix of researchers, vendor-based practices, and practitioners over the last two to three years, and you’ll find real disagreement about where one ends and another begins. Treat what follows as a useful working guide, not a bulletproof spec.

The Short Version of Definitions

• SEO (Search Engine Optimization) helps eligible content get crawled, indexed, and ranked in traditional search results.
• AEO (Answer Engine Optimization) aims to make content easy for answer-oriented systems — featured snippets, voice assistants, answer boxes — to extract and present. No particular format guarantees selection.
• GEO (Generative Engine Optimization) aims to improve the likelihood that generative search systems can find, use, attribute, or summarize your content.
• LLMO (Large Language Model Optimization) is a commonly used label for entity clarity and accurate representation — helping a model describe your brand, project, or name correctly when it’s drawing on what’s publicly known about you.

They’re four angles on the same question — will an algorithm or a model find, understand, and choose to use my content? — applied at different points in the content discovery pipeline.

Defining Each One

SEO (Search Engine Optimization)

The one you should already know. Keyword targeting, backlinks, site speed, crawlability, clean information architecture. You can start with this from (Mozilla’s MDN documentation)[] and continue on a deeper dive with resources like Google’s Search Central. The output, when it works, is a ranking in a list of links a human scans and clicks. Every acronym below assumes SEO fundamentals are already in place — a slow, unindexed, poorly structured page is unlikely to perform well in any of these systems, not just Google’s.

AEO (Answer Engine Optimization)

AEO is about structuring content so a search engine or voice assistant can lift a precise answer out of the page — a featured snippet, a definition box, a spoken response from Apple’s Siri or Amazon’s Alexa. It grew out of question-based search behavior. The tell or the structure is in the format: short definitions near the top, question-style headers, FAQ-style sections, numbered steps. This type of content eligible for these placements; it doesn’t guarantee them. Selection still depends on the platform, the query, and competing content.

GEO (Generative Engine Optimization)

The one we’ve already covered in depth, so the short version: GEO is about structuring and substantiating content so generative engines — ChatGPT, Perplexity, Kimi, Google’s AI Overviews, Gemini, among others — are more likely to find it, use it, and attribute or summarize it in a response. Where AEO tends to reward being short and precise, GEO tends to reward depth, sourcing, and topical authority. The term was popularized by a 2023 research paper from authors affiliated with Princeton University and IIT Delhi — it’s a useful framework, not an officially standardized discipline, and different platforms weigh different signals.

LLMO (Large Language Model Optimization)

LLMO is the newest and most misunderstood of the four. It’s a commonly used label for work aimed at entity clarity — helping a model represent your brand, project, or name accurately and consistently when it’s asked about you. It is not a mechanism for directly controlling a model’s training data or persistent memory; publishing content doesn’t rewrite what a model already learned. In practice, AI-mediated answers can draw on live retrieval, search indexes, third-party sources, and, in some cases, prior model knowledge — publishers influence some of these inputs and don’t control others. LLMO in practice looks less like optimizing a single page and more like maintaining consistent naming, bios, and public information about an entity across the sites, docs, and platforms a model or retrieval system might encounter.

Before You Optimize

Before layering AEO, GEO, or LLMO tactics on top, it’s worth confirming the fundamentals are actually in place — most AI-visibility problems trace back to one of these:

• Indexable, and where appropriate server-rendered, HTML
• Correct robots.txt and meta-robots rules (including any explicit allow/disallow policy for AI crawlers, set intentionally rather than by default)
• Canonical URLs
• A maintained XML sitemap
• Clear internal linking between related pages
• Accurate, specific titles, headings, and visible on-page content
• Structured data that matches what’s actually visible on the page — not aspirational markup for content that isn’t there
• A stable identity page for the author, organization, or project (bio, canonical bio links, consistent naming)

Skipping this layer and going straight to schema markup or “AI-optimized” content is a common mistake — none of the acronyms below can compensate for a page a crawler can’t reach or parse.

How They Actually Compare

Extending the table from the first post in this series:

Dimension	SEO	AEO	GEO	LLMO
Discovery Method	Search engines crawl, index, and rank	Answer-oriented systems extract a direct response	Generative systems retrieve, use, and may attribute or summarize content	Models and retrieval systems draw on public information to describe an entity
Content Focus	Keyword-relevant, well-structured long-form content	Clear on-page answers, question-led headings	Well-sourced, substantive, verifiable answers	Consistent terminology and entity clarity
Goal	Helps eligible content get crawled, understood, indexed, and ranked in search results	Make content easy for answer-oriented systems to extract and present; no format guarantees selection	Improve the likelihood that generative search systems can find, use, attribute, or summarize content	Improve accurate, consistent representation across AI-mediated discovery
Metrics	Rankings, clicks, conversions	Snippet/answer-box appearances, voice answer share	AI-search referral traffic, visible source links, appearance across a repeatable sample of target prompts, branded-search lift	Accuracy and consistency of AI-generated descriptions or mentions
Technical Needs	Metadata, page speed, backlinks, crawlability	Clear on-page answers, logical headings, lists and tables, semantic HTML, and relevant supported structured data	Structured data, semantic HTML, freshness, citable sourcing	Consistent naming, author/organization identity pages, cross-site entity signals

A quick note on GEO “attribution,” since the term gets used loosely: being referenced by a generative system can mean different things depending on the product — an inline citation, a visible source link or card, AI-search referral traffic to your site, an unlinked summary of your content with no visible attribution at all, or simply a branded mention with no link. These aren’t interchangeable, and a platform that shows one doesn’t necessarily show the others.

Read across the table and the overlap is the real takeaway, more than any individual row: a well-structured, well-sourced, technically sound page tends to perform reasonably across all four lenses at once.

So Which One Do You Actually Need?

You probably don’t need four separate strategies or four separate content calendars. What deserves the most attention depends on what you’re building, remember “it depends” :

• Shipping docs or a how-to guide? Lean on AEO habits — clear structure, question-led headings, and a tight, extractable answer near the top.
• Publishing a deep technical breakdown, a case study, or a research-backed post? Lean on GEO habits — depth, citations, and enough substance that a generative system has something verifiable to draw from.
• Building a personal or company brand across talks, repos, and articles? LLMO is the long game — consistent naming, a clear bio, and showing up the same way across platforms so a model or retrieval system has less to guess at.
• Building anything public-facing at all? SEO fundamentals aren’t optional. None of the above compensate for a slow, unindexed, badly structured site.

In practice, most developer content ends up touching all four if it’s written well: a clear definition near the top (AEO habits), backed by real depth and sourcing (GEO habits), using consistent terminology about your project or yourself (LLMO habits), on a fast, crawlable page (SEO fundamentals).

One more caveat worth repeating: crawling, indexing, retrieval, citation, linking, and attribution behavior all vary by platform and change over time as these products evolve. The realistic goal isn’t forcing a specific answer outcome — it’s making content easier to crawl, understand, verify, retrieve, and select, and then observing what actually happens.

Why This Matters for Developers

The same reasoning from the first post in this series still holds, just with a wider lens now: understanding why content gets found and used — not just by search engines, but by the AI systems increasingly standing between users and the web — is a genuine edge. That’s true whether you’re writing documentation, building a portfolio, publishing technical articles, or just trying to make sure an AI-generated answer about your own work is accurate.

Bridging structure, code, and content strategy isn’t a marketing skill bolted onto engineering work. Increasingly, it’s part of the engineering work.

In Short

SEO, AEO, GEO, and LLMO aren’t four competing strategies — they’re overlapping lenses on the same discovery pipeline: getting crawled and indexed, becoming eligible for extracted answers, becoming eligible for use or attribution in generated summaries, and being accurately represented when a model or retrieval system describes you. Structure your content well, go deep where depth builds trust, keep the fundamentals solid, and stay consistent about who you are across every platform — and you end up covering most of this ground without needing to treat any one acronym as the whole strategy. Terminology here is still unsettled and measurement is still fragmented across platforms; treat specific tactics as informed bets, not guarantees.

Key Learning Resources

• Google Search Central: Structured Data Guidelines — the baseline for how structured data is meant to be used, including its limits.
• Schema.org — the shared vocabulary most structured-data markup draws from.
• “GEO: Generative Engine Optimization” — the original 2023 research paper (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande) that introduced and studied the term.
• OpenAI Platform Documentation and Anthropic Docs — for understanding how these systems process and cite structured versus unstructured content.
• Google Search Central: Robots.txt Guide — for setting intentional crawler access policies, including for AI crawlers.

Call to Action ~ Your Turn

Pick one piece of content you already have — a README, a blog post, a talk abstract — and run it through all four lenses. Is it structured clearly enough to be extractable (AEO habits)? Substantive and sourced enough to be worth citing (GEO habits)? Consistent enough that an AI system describing you would get it right (LLMO habits)? Actually crawlable and indexable in the first place (SEO fundamentals)? Fix the weakest layer first, treat the rest as an ongoing practice rather than a checklist, and expect the specifics to keep shifting as these platforms do. Progress over perfection.

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This post is a companion piece to “Beyond SEO: Optimizing Your Web Content for the Age of AI,” a talk on GEO for developers. Slides and more at developingdvlpr.com and a running log of talks and projects at https://learnt.developingdvlpr.com/.