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The growth and usage of AI-first tools like ChatGPT, Perplexity, Claude, and Google’s AI Overviews has changed how people search and discover information online. Instead of traditional search engine result pages (SERPs), users now engage with conversational interfaces that deliver summarised, often citation-free answers. This shift marks the beginning of a new discipline called LLMO, or Large Language Model Optimisation. While traditional Crypto SEO remains relevant, its dominance is fading in favour of strategies designed to make content more accessible, referenceable, and trustworthy to AI models. Brands must now optimise not only for ranking but for comprehension.
SEO vs LLMO: What’s Changed
| SEO (Traditional Search) | LLMO (AI and LLM Search) |
|---|---|
| Ranks pages by keywords, links, speed | Surfaces answers based on authority, context, and clarity |
| Optimises for Google or Bing results | Optimises for LLM citations in tools like ChatGPT, Perplexity, Gemini |
| Focuses on domain and page rank | Focuses on entity authority, authoritativeness, and language clarity |
| Click-through rates and impressions from search results | Visibility in AI summaries and conversational answers |
| Crawlability and indexation are critical | Understandability and summarisation are more critical |
| Content is the destination | Content becomes the source for answers |
Traditional SEO was about visibility in search engines, competing to appear in the top results for a keyword query. LLMO for Crypto & Web3, by contrast, is about surfacing within AI-generated answers. Rather than directing users to your site, LLMs repackage your content into conversational output. This changes what visibility means and requires rethinking how content is created, structured, and distributed.
Brand Relevance Over Domain Authority
While SEO traditionally leaned on domain authority influenced by backlinks, trust signals, and site history, LLMs lean more on brand prominence and entity recognition. They interpret how often and how credibly a brand or product is mentioned across diverse content ecosystems, including technical forums, GitHub repositories, developer blogs, and social platforms. Brand-level recognition is increasingly weighted more than raw domain metrics because LLMs associate trust with real-world reputation rather than link patterns.
| SEO Focus | LLMO Focus |
|---|---|
| Backlink quantity and domain trust | Brand and entity visibility and mention quality |
| Authority passed from linking sites | Authority inferred from broad web context |
| Optimise for PageRank flow | Optimise for entity consistency and presence |
From Keywords to Concepts
Search engines are built around query-to-keyword matching and contextual relevancy. LLMs take a more semantic approach and generate answers by mapping and interpreting concepts. Content must be structured and written to explain topics clearly rather than simply trying to rank for keyword clusters. LLMs pull from multiple sources to formulate answers and will favour content that communicates meaning, relevance, and reasoning with precision.
| SEO Focus | LLMO Focus |
|---|---|
| Target keyword density and placement | Clear concept explanation and reasoning |
| Meta titles, headings, and keyword clusters | Natural language, structured summaries, and internal coherence |
Content Structure Optimised for Summarisation
AI models do not just scan for links or metadata. They attempt to comprehend and rearticulate ideas. Pages that are written in a structured, modular format are easier for LLMs to summarise and quote. Content should be broken into logical segments using semantic HTML, clear headings, lists, and structured explanations. The goal is not only human readability but machine summarisation.
| Poor LLM Structure | LLM-Friendly Structure |
|---|---|
| Long paragraphs with little hierarchy | Clear subheadings and bullet points |
| No summaries or contextual introductions | TLDR sections and context-rich blocks |
| Dense technical language | Accessible language with examples and analogies |
Entity Authority Over Page Rank
In an LLM-driven ecosystem, models interpret authority based on a source’s presence and reliability across the entire web. It is no longer enough to have a single, well-optimised article. Brands and individuals must build entity-level authority with sustained topical expertise, consistent voice, and cross-platform presence. Authority is established through broad and reliable representation in trusted datasets that LLMs learn from including documentation, open source projects, and citations on respected platforms.
| Traditional SEO | LLMO |
|---|---|
| One blog post can rank well | LLMs assess entire knowledge footprint |
| Content is judged in isolation | LLMs connect content across sources, authors, and brands |
Author and Source Signals Are Crucial
Authorial identity plays a growing role in whether content is surfaced by LLMs. While SEO traditionally focused more on site-level authority, LLMs seek to emulate how people trust real experts. They prioritise verifiable expertise, transparent attribution, and source credibility. Articles written by credible authors with schema markup and evidence of subject-matter authority are far more likely to be cited than anonymous or generic content.
| Weak Signals | Strong Signals |
|---|---|
| Anonymous or generic posts | Named author with verified expertise |
| Minimal or placeholder bios | Detailed schema markup and reputation trails |
| No outbound citations | Thoughtful linking to credible sources |
LLMO Is About Being the Source Not Just Ranking
The most important shift in the AI-powered search era is that content is no longer just a destination. It becomes a source of answers. Rather than drawing users into your funnel via clicks, the new objective is to have your content quoted or paraphrased in the LLM’s response. Content should be definitive, accurate, and capable of standing on its own within a summarised answer. This means creating original insights, canonical explanations, and up-to-date commentary that machines trust and reference.
| Old Goal | New Goal |
|---|---|
| Rank at the top of search engines | Be cited by LLMs as a trusted source |
| Compete for keyword-based clicks | Win inclusion in AI-generated answers |
Technical SEO Still Matters But Differently
While performance, crawlability, and site hygiene remain important, LLMO shifts the technical focus toward semantic clarity and machine interpretation. This includes structured data, clean headings, and internal conceptual consistency. Pages that are technically well-structured and logically scoped are easier for LLMs to understand, cite, and trust in answer generation.
| Classic SEO Priority | LLMO Priority |
|---|---|
| Fast load times and indexable pages | Contextual clarity and modular structure |
| Internal linking for PageRank flow | Internal topic hierarchy and navigation logic |
| Sitemaps and robots directives | Purposeful content layout and machine-readable schemas |
Real Tactics for LLM Optimisation
| Tactic | Why It Works |
|---|---|
| Add TLDRs and summaries to every page | Helps LLMs identify and extract key ideas |
| Create concept pages that define terms | Enhances topical authority and citation potential |
| Publish developer documentation and use-case guides | High trustworthiness in technical content domains |
| Use structured data to mark up articles and authors | Increases machine readability and source credibility |
| Track citations from ChatGPT, Perplexity, and RankLens | Measures actual presence inside AI-generated content |
| Distribute content to GitHub, Reddit, and Twitter | Expands brand footprint across high-trust ecosystems |
What to Do Next
- Review your existing content for structure, clarity, and usefulness from the perspective of a summarising AI model.
- Ask common queries about your brand, product, or category in ChatGPT, Gemini, and Perplexity and examine whether your brand appears in the answers.
- Convert long-form or keyword-heavy content into formats that favour summarisation such as guides, explainers, and FAQs.
- Build out author pages, link bios across trusted domains, and mark up authorship using schema.
- Monitor changes in how your content appears or disappears in LLM outputs and refine based on actual citation behaviour.
Final Thought
Search is no longer just about visibility in search engines. It is about understanding and citation within AI models. Traditional SEO focused on drawing users to your website. LLMO ensures your message appears inside the answer itself. This means becoming a trusted authority across the web, structuring your content for clarity, and shifting from keyword-driven tactics to AI-native strategies. The future of discoverability is conversational and it is already here.
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