TL;DR: AI search has spawned a confusing set of acronyms, ALLMO, LLMO, GEO, AISO, AIO, and more, as organizations shift from optimizing for ranked links to being cited in AI-generated answers. This guide defines each term, explains why they emerged, recommends using LLMO as your primary practice name, and provides a practical framework to execute and measure AI search optimization. Search queries posed to AI chatbots are now twice as long as traditional keyword searches, signaling a fundamental shift in how users seek information.

Why We Have This Alphabet Soup Now: AI Answers Are Changing Search

The explosion of AI search acronyms reflects a fundamental transformation in how information is discovered and delivered online.

AI systems increasingly generate direct answers rather than lists of links. Platforms like ChatGPT, Perplexity, and Google's AI Overviews synthesize information from multiple sources and present it conversationally, often with attribution to licensed or integrated content partners. This shift means optimization now focuses on being included and trusted in AI outputs, not just ranking high in traditional search results.

Zero-click behaviors are rising as users receive complete answers without navigating to source websites. When an AI chatbot answers a question about skincare ingredients or CRM integration directly, the user never clicks through. Content must be recognizable to both large language models and retrieval systems to appear in these answers.

As this new category of search optimization emerges, so does the need to define and name it. Over the past months, a variety of terms have emerged.

Glossary: The Core AI Search Acronyms, in Plain English

Understanding the terminology is essential before building a strategy. Here's what each acronym means and where it fits.

ALLMO (Applied Large Language Model Optimization) is the practice of optimizing content so it is recognized, understood, and cited by large language models in generated AI responses. The term emphasizes practical application of optimization techniques to make content visible in AI answer engines, going beyond traditional keyword-focused SEO.

LLMO (Large Language Model Optimization) refers to tailoring content for visibility and citation in AI systems powered by LLMs like GPT or Gemini. It focuses on how models use both training data and live retrieval (RAG) to produce answers. LLMO and ALLMO are essentially synonyms, with LLMO being more widely adopted due to its clear tie to "Large Language Models"

GEO (Generative Engine Optimization) is another term for the same practice—optimizing content for AI engines that generate answers instead of producing ranked search listings. GEO emphasizes the generative aspect of modern AI search but carries a risk of confusion with geography-related marketing (geo-targeting, local SEO).

AISO (Artificial Intelligence Search Optimization) and AIO (Artificial Intelligence Optimization) are loosely defined umbrella terms that overlap with LLMO and GEO. AIO is particularly ambiguous: it's sometimes used to mean "Artificial Intelligence Optimization" in blog posts, but is also widely used as shorthand for Google's AI Overviews product—a platform label, not a practice name. This dual meaning creates confusion in formal documentation.

SEO (Search Engine Optimization) remains the traditional practice of optimizing web content to rank well in keyword-based search engines. SEO focuses on crawlability, keywords, backlinks, and ranking positions. It is still essential for driving traffic from ranked listings, but insufficient alone in AI answer contexts where the goal is citation and inclusion, not just ranking (ALLMO, 2024, https://allmo.ai).

AxO does not have an established definition in this space. Occasionally used as a placeholder pattern ("X Optimization") in early discussions, it is not a recognized standard and should be avoided in formal strategy documents until a consensus definition emerges.

Adjacent Terms You'll Encounter (and How They Fit)

Several technical and platform-specific terms orbit the core acronyms. Understanding how they relate prevents confusion and clarifies where they fit in your strategy.

RAG (Retrieval-Augmented Generation) is a technical method that fetches external sources in real-time to ground LLM answers. Unlike models trained on static datasets, RAG-enabled systems retrieve the most current, relevant information to supplement the model's knowledge. This impacts how content should be structured and stored: RAG systems favor well-organized, authoritative pages with clear headings, stable URLs, and structured data (Search Engine Journal, 2024, https://www.searchenginejournal.com).

SGE/GSE (Search Generative Experience) and AI Overviews are platform labels for Google's generative search experiences. These are product names describing how Google surfaces AI-generated answers in search results. They are relevant to channel strategy and where to allocate optimization effort, but they should not be used as the name of the practice itself—just as you wouldn't call your SEO strategy "Google Optimization."

AEO (Answer Engine Optimization) is a legacy term from the era of voice search and featured snippets. It overlaps conceptually with GEO and LLMO. focusing on optimizing for direct answers, but is less commonly used in LLM contexts and often associated with older voice assistant technologies.

Multimodal search refers to engines that retrieve and reason over text, images, video, and audio. AI systems increasingly integrate diverse content types to build answers. Microsoft Azure AI Search, for example, combines text and image retrieval to support richer, more comprehensive responses. Optimization must extend beyond text to include visual metadata, alt text, transcripts, and multimedia structured data.

How the Acronyms Relate: A Simple Taxonomy to Avoid Talking Past Each Other

To reduce confusion and align your team, use this simple framework to categorize the terms.

Practice level (choose one): LLMO and GEO both describe the practice of optimizing content for AI answers. Treat them as near-synonyms focused on LLM visibility and citation. LLMO is the clearer, less ambiguous choice. ALLMO is a branded variant emphasizing "applied" optimization but functions identically to LLMO in scope.

Platform labels: SGE, GSE, and AI Overviews describe specific product experiences from Google and other vendors. These are useful for discussing where to optimize (e.g., "Our content appears in Google AI Overviews"), but do not use them as the name of your practice or in KPI dashboards.

Technical methods: RAG, embeddings, vector search, and multimodal retrieval are implementation details that inform how AI systems work. They shape your optimization tactics (e.g., "structured data improves RAG retrieval"), but they do not rename the practice. Think of them as the "how" behind LLMO, not the "what."

This taxonomy provides a shared language for cross-functional teams, preventing the scenario where engineering discusses "RAG," marketing discusses "GEO," and leadership discusses "AIO", all referring to overlapping but poorly aligned concepts.

Expect more acronyms to emerge. As AI search matures, specializations will spawn new terms, perhaps for video optimization, for voice-first assistants, or for vertical-specific AI engines. Maintain an internal glossary, document definitions centrally, and revisit terminology quarterly. The key is consistency within your organization, even as the industry experiments externally.

FAQ: Quick Answers to Common Acronym Questions

Is SEO dead?

No, traditional SEO remains essential for ranked discovery in search engines. Users still click through to websites from Google, Bing, and other search results pages. However, LLMO adds a second, parallel channel focused on appearing in AI-generated answers. Both practices are necessary, and they share many foundational elements (authority, structure, freshness). Organizations that excel at SEO have a strong foundation for LLMO.

LLMO vs GEO vs ALLMO, what's the difference?

LLMO (Large Language Model Optimization) and GEO (Generative Engine Optimization) are near-synonyms describing the practice of optimizing for AI answers. ALLMO is a variant emphasizing "applied" optimization but functionally identical to LLMO. The primary difference is naming preference: LLMO is clearer and less prone to confusion with geography-related marketing, making it the recommended choice for internal standardization.

What about AIO and AISO?

AIO and AISO are loosely defined and sometimes conflict with platform names (e.g., AIO is widely used to mean Google's AI Overviews product). They lack consensus definitions and create ambiguity in formal strategy documents. Avoid these terms unless you define them explicitly in your specific context, otherwise, default to LLMO or GEO for clarity and consistency.

What is AxO?

AxO is not a recognized standard in AI search optimization. It appears occasionally as a placeholder pattern ("X Optimization") but lacks an accepted definition or industry adoption. Treat it as speculative and avoid using it in formal strategy, OKRs, or vendor discussions until a consensus emerges.