Large Language Models like ChatGPT, Claude, Gemini, and Perplexity are reshaping search. Instead of SEO for search engines, brands now need LLM optimization, preparing content so that AI tools can find and cite it. In this era of AI-driven discovery, visibility means being a trusted source for machine answers. For example, in early 2025 Google reported 1.5 billion monthly users getting answers via its AI Overviews. At the same time, ChatGPT reached 600 million users and Gemini reached 350 million. These trends show people increasingly ask chatbots instead of traditional search. To stay visible, brands must adapt by optimizing not just for pages on Google, but for mentions and citations by LLMs.
Today, AI platforms summarize content across the web. Brands must make high-value information easy for LLMs to find and cite. In practice, LLMs process natural language and context via advanced NLP to answer user queries. Unlike older SEO, which tweaks pages for search-engine rankings, LLM optimization focuses on semantic clarity and authority. It means structuring content so AIs can extract useful answers directly. LLMs often summarize multiple sources rather than one, so only content that’s concise, accurate, and well-cited will be used.
Traditional SEO aims to rank high on Google. LLM optimization aims to rank within AI answers. 60%+ of informational queries now display an AI-generated summary. When an LLM answers a question, it cites websites as sources. Only websites deemed trustworthy get those citations. So even high-ranking pages can lose visibility if AI tools don’t consider them authoritative. As a result, brands that aren’t present in AI answers risk traffic loss and fading authority.
The new acronym LLMAO takes this further. LLMAO emphasizes optimizing content so that LLMs give better answers more naturally. For example, Google’s SEO community hints that future SEO might even officially be called LLMAO. Whether you call it LLMO, LLMAO, or GEO, the goal is the same: build content and authority that LLMs trust and cite.
To thrive in AI search, brands should adopt a multi-faceted approach. Here are the core tactics that should be used and recommended to all marketing leaders:
LLMs rank sources by credibility. Encourage being cited in reputable online venues, industry blogs, news articles, reputable forums, or social Q&A. For example, user-generated content on Reddit, Quora or LinkedIn groups can be especially powerful. Google even uses Reddit answers to train its AI models. When ChatGPT answers a question, it often cites Reddit as a source.
In practice, run AMAs, answer niche questions on forums, or use HARO to get expert quotes in the press. These citations link your brand to the right topics, signaling LLMs that your content is trustworthy. Digital PR publishing data studies or collaborating with industry press, also generates authoritative backlinks and mentions. Each mention or link is like a citation that increases your entity popularity for LLMs.
LLMs understand content by concepts, not just keywords. Make sure your brand’s core promise is clear and consistent across the web. Appearing in the Knowledge Graph helps AI trust your brand. In practice, optimize your own site’s branding and press so that all mentions of your company name, people, and products form a clear picture. Use consistent NAP citations and descriptive language. The goal is that when an LLM sees your brand name, it immediately associates it with the right topics and uniqueness.
Create content filled with meaningful data, clear conclusions, and quotes. LLMs cite content with factual authority. So embed statistics, case studies, or original research with proper attribution. Write concise, standalone statements that directly answer key questions. For instance, start sections with clear answers, then explain further. Additionally, use bullet lists and tables for clarity. Each paragraph should convey one idea in 2–4 sentences. This style not only improves human readability, it helps LLMs extract snippets for answers. In short, pack each paragraph with a single, quotable insight, then LLMs can easily use it as a source.
LLMs index the web differently than humans. They read raw HTML and text, they don’t click through paywalls or execute heavy scripts. Make your content fully crawlable, which means no hidden paywalls or gated content. Publish in standard HTML. Use proper title tags and alt text so that AI sees the context. Optimize page speed and mobile-responsiveness, as these improve crawling.
While schema markup may not directly influence AI models, it helps structure your content logically. In practice, avoid relying on JavaScript-only content or login walls. The sooner LLM crawlers can scan your page, the sooner they can recommend it.
LLMs are becoming multimodal, they can parse images, audio and video if properly described. Add descriptive alt text to every image. Include charts or infographics with explanatory captions. If you host videos, include full transcripts. AI assistants often quote from transcripts when generating answers.
The more contexts you give LLMs beyond raw text, the richer their answers. For example, an infographic on LLM optimization steps with embedded keywords helps the model surface that info. But remember: always accompany the media with text explanations. LLMs skip purely decorative images without alt text.
Avoid large, dense paragraphs. This means that for LLM Optimization, your content should use short, clear sentences, start each section with a direct answer before adding details, and keep each paragraph focused on a single idea, making it easy for AI models to extract and cite your information. This conversational style is exactly how chatbots present info.
Use questions and answers inside your content. For example, include FAQ headings or Q&A bullet points that directly restate common user questions. Breaking content into small, focused sections helps both human readers and LLM engines grasp and cite your points. Aim for each paragraph to be self-contained: if an AI picks it up, it should stand alone as a complete snippet.
AI tools personalize content by detecting user intent and background. Ensure your content speaks clearly to each key audience. If you’re targeting marketing leaders, SEO pros, startup founders, or content creators, tailor your language and examples accordingly. Use persona-specific terminology and contextual scenarios. For instance, mention SEO professionals when discussing keywords, or startup founders when discussing growth. This segmentation boosts relevance signals.
When users interact with AI chat, their behavior will favor content that feels directly relevant. Think of it as writing multiple mini-articles under one umbrella: one section for each segment’s needs. LLMs will surface the segment that best matches a particular user’s query and context.
Just as consumers form opinions, LLMs form opinions about brands based on available info. Pay attention to brand mentions on social media, forums, and review sites. Use reputation-management tools to ensure facts are correct and favorable. If negative or false info appears online, address it promptly. LLMs pick up on overall tone in their training data; a brand with positive sentiment and authoritative content will be more likely recommended.
By combining these strategies, your brand will be positioned not just for clicks, but for citations by AI assistants. This doubles your visibility which means you get traditional search traffic and AI-driven referrals. Brands that fail to optimize for LLMs risk slipping below AI’s radar as Google’s market share gradually declines in favor of chat search.
Think of LLM Optimization as the next evolution of SEO. Most best practices still apply – great research, clear meta tags, internal links, and backlinks all help signal expertise to AI models. But today, the unit of visibility is smaller. Individual facts, quotes, and examples are now lifted directly into AI answers. That means our workflow must evolve. We need to create content with AI in mind first. This demands clear answers, structured sections, entity-rich context, and then ensures it’s easily indexed by search engines.
A strong LLMO strategy helps brands evolve from simply being found to being recommended by AI. By combining clear, well-structured content, technical best practices, and credible digital presence, you naturally position your brand as reference-worthy, so when someone asks an AI, “Who’s the leader in your space?”, your name is part of the answer.
1. How do you get your brand to appear in ChatGPT?
Make your content LLM-ready with strong LLM Optimization so ChatGPT finds, understands, and cites it. Get mentioned in trusted sources and write clear, quotable passages.
2. How to get featured in ChatGPT?
To get featured in ChatGPT answers, focus on LLMO and NLP best practices: publish factual, accessible, and entity-rich content that credible sites link to.
3. How to get ChatGPT to recommend your brand?
LLMs like ChatGPT recommend brands they trust, so build citations, use LLM Optimization, and maintain positive brand sentiment across the web.
4. How do you get your company listed on ChatGPT?
You can’t list your company directly. Instead, use LLMO to ensure your company’s content is crawlable, cited by authoritative sources, and easy for ChatGPT to pull.
5. How to get Gemini to show sources?
Gemini shows sources when it trusts the content, so follow LLMO principles: publish original, factual content, optimize your site for NLP, and earn credible backlinks.
Sandeep Israni is the co-founder of BrightBrain Marketing Technologies LLP – a firm that has now grown into a full-fledged digital marketing agency with 55+ employees and clients from 7+ countries – all under his supervision. He mastered the art of Executive Management at SP Jain Institute of Management & Research, followed by extensive industry experience. His unquestionable expertise allows him to help clients achieve the extraordinary in the digital realm.
