You’ve done the hard work - optimised pages, built backlinks, ticked every SEO box. Google shows you on page one. Bing picks you up, too. But then you type the same query into ChatGPT or Perplexity… and your brand is nowhere?
This is because the way traditional search engines retrieve and display information differs significantly from how AI-driven search (LLMs) does it. That difference impacts what kind of content gets surfaced and cited, and therefore, what optimisation needs to look like going forward.
No, we aren’t saying SEO is dead, but so far, you have only optimised for users; now you need an additional layer of actions solely for AI/LLMs.
Let’s break it all down, from bots to search cycles, and finally, to what SEO pros and even small/medium businesses can actually do.
Here’s the order of tackling we will be following:
Table of Contents
Let’s start with the bots, because understanding the difference in their behaviour matters.
Bots (Crawlers vs LLM bots)
In the traditional search world, crawlers do the heavy lifting.
Googlebot, Bingbot, DuckDuckBot: These are librarians. They crawl billions of web pages, organise them into a catalogue (the index), and when someone searches, the engine looks inside that catalogue, weighs signals like keywords, backlinks, freshness, and authority, and ranks the results.
In the AI world, bots behave differently.
ChatGPT Browse, Perplexity bot, Claude’s web search: These are researchers. They don’t bother cataloguing every book in advance. Instead, when you ask a question, they sprint out, fetch a few relevant sources in real time, skim them, and generate an answer. Sometimes they’ll cite those sources, sometimes they won’t.
So yes, both sides use “bots.” But crawlers organise the entire library, while LLM bots fetch what you need in the moment.
If I’m already optimising for human users, people who also search, skim, and read answers, then what’s really different about optimising for LLM bots, who behave similarly?
Great question!
Here’s how it’s different:
Humans skimming
We rely on visual cues, literally see bold, colour, layout, design, and use that to decide where to look.
We bring Context & prior knowledge; life experience, brand familiarity, and even bias (“I trust NYTimes more than a random blog”).
We handle nuances; If a sentence is vague, we fill in the blanks with reasoning.
For trust, we compare tone, credibility, design, and our gut feeling across multiple pages before trusting.
AI bots Skimming (LLM retrieval)
They rely on patterns in text like headings, schema, sentence structure, and semantic signals.
They don’t “understand” mixed information vs insight like a human; they prefer clear, concise, fact-like statements.
They lack human judgment; if data looks structured/precise, it gets weight, even if it’s not the best.
They often only skim a handful of sources fetched in real time.
So the difference is:
Humans skim with intuition + lived experience.
AI skims with patterns + probability (is this text answer-like?).
That’s why citation-friendly writing matters. If you give the AI short, precise, fact-ready sentences, you increase the odds it lifts your content instead of a competitor’s.
The Cycles: How Search Engines vs LLMs Work
If you just want the gist, the difference between crawlers and LLM bots already tells the story. But if you’re curious about the step-by-step process, here’s how their cycles actually play out.
Traditional Search Engines
Google (classic search):
Crawling: Googlebot continuously scans the web.
Indexing: Pages are stored in Google’s index.
Ranking: Algorithms weigh hundreds of signals (keywords, links, authority, freshness).
Results: A ranked list of links, sometimes enriched with snippets, panels, or images.
User: Clicks a link and consumes the content directly on the site.
Bing:
Similar to Google, Bingbot crawls and indexes pages.
Ranking: Combines SEO signals with user interaction data (click patterns, dwell time).
Results: Blue links, multimedia, direct answers.
User: Clicks through to a site.
DuckDuckGo:
Crawling: DuckDuckBot plus Bing’s index + other partners.
Ranking: Signals like relevance and authority, but no user profiling (privacy-first).
Results: Blue links, instant answers (e.g. Wikipedia, APIs).
User: Clicks through without being tracked.
Common points: Crawling, indexing, and users routed through blue links.
AI-Integrated Search (Google AI Overviews, Gemini)
Google AI Overviews represents a bridge between traditional search and LLMs.
Crawling & Indexing: Still powered by Googlebot.
AI Processing: Your query also runs through Google’s Gemini AI.
Answer Snapshot: At the top, you see an AI-generated summary that condenses info from multiple sources.
Supporting Sources: Links are shown underneath or alongside the snapshot.
User: Can either read the AI’s quick answer or click through to explore sources.
Here, the traditional index is still the backbone, but AI adds a layer of summarisation.
AI Mode has just a few extra steps integrated due to its chat-based UI, but it is still powered by GoogleBot
Query: User types a conversational/multi-part question.
Decomposition: AI breaks the query into sub-questions.
Retrieval: Pulls info from Google’s index + Knowledge Graph.
Filtering: Scores and selects high-quality, trusted, fresh sources.
Answer Snapshot: AI generates a snapshot/answer from multiple documents.
Display: Shows the summary at the top, with source links below.
Follow-ups: The User can continue asking in context.
Fallback: If uncertain, AI falls back to standard search results.
LLM Search Engines
ChatGPT (Web-Enabled)
Retrieval: ChatGPT sends the query to Bing (Google not confirmed)
Fetch: A handful of pages are pulled in real time.
Parsing: Pages are skimmed for useful details.
Generation: ChatGPT blends what it found with its training data to generate a natural-sounding answer.
Citation: Sometimes shown, sometimes omitted.
Perplexity:
Retrieval: Real-time search via Bing’s API.
Fetch: Pulls multiple pages.
Parsing: Compares across them.
Generation: Produces a consolidated answer.
Citation: Always shown inline, source-first approach.
Similar to ChatGPT, but always cites or shows the source link.
Copilot (Microsoft, powered by Bing + GPT-4 Turbo):
Retrieval: Bing search runs in real time.
Parsing: Extracts relevant snippets.
Generation: GPT-4 Turbo generates the response.
Citation: Provided inline, tied directly to Bing results.
Claude (Anthropic, with Web Search):
Starts with its training data.
If new info is needed, it fetches sources Live.
Reads them selectively and blends with context.
Citations tend to be fewer but from higher-trust domains.
Mistral (via Le Chat and partners)
Browsing is API-driven.
Fetches a handful of sources when enabled.
Generates concise answers.
Citations depend on the product wrapper.
To summarise the cycles:
Traditional search = pre-built index + ranking.
Google AIO and AI Mode = index + AI summary.
LLM search = real-time fetch + generated answer.
Difference between SEO and GEO Optimization
Okay, I understand how they work, but what is the difference when it comes to optimization? Are there truly more things that need to be done?
Let’s get into that, shall we?
What SEO Pros Already Do
SEO pros have built their craft on three pillars: On-page, Off-page, and Technical. These remain essential.
On-page: Writing keyword-targeted content, optimising titles and meta tags, building topic clusters, and adding structured data.
Off-page: Link building, guest posting, digital PR, partnerships, and social signals.
Technical: Ensuring crawlability, site speed, mobile responsiveness, sitemaps, robots.txt, and fixing broken links.
E-E-A-T: Proving expertise, experience, authority, and trust with author bios, reviews, and transparent sourcing.
These are the basics. Without them, you won’t rank in Google or Bing.
What Needs to Be Added for GEO / LLMO
For AI-driven search, optimisation shifts from being “rankable” to being citable.
Answer-ready content: Add FAQs, Q&A blocks, and TL;DR summaries that LLMs can lift directly.
Entity clarity: Strengthen your brand’s presence in structured data (Wikidata, Crunchbase, LinkedIn, schema). The more AI systems “recognise” you, the more likely you’ll be cited.
Semantic coverage: Go beyond keywords. Cover related questions, synonyms, and concepts. LLMs understand meaning, not just phrasing.
Freshness: Update articles regularly. Add “last updated” labels. Publish new data or commentary. AIs tend to prefer recent content.
Original insights: Share unique stats, case studies, surveys. Original numbers stand out and get cited.
Citation-friendly writing: Put key facts into short, quotable sentences instead of burying them in human-readable paragraphs.
Accessibility: Keep content open to bots, avoid blocking AI agents, and don’t hide key info behind paywalls or JS-heavy pages.
This doesn’t replace SEO. It builds on top of it.
In Short,
On-Page
What we do in SEO: Titles, meta tags, keyword-rich content, headers, schema.
What needs to be added for GEO: Add FAQs, semantic context, direct answers, and short quotable facts.
Off-Page
What we do in SEO: Link building, digital PR, and social signals.
What needs to be added for GEO: Focus on entity mentions in trusted sources (news sites, .gov/.edu domains, industry authorities). Publish original research that others will cite.
Technical
What we do in SEO: Core Web Vitals, crawlability, mobile optimisation, and sitemaps.
What needs to be added for GEO: Allow AI bots, highlight freshness clearly, use structured APIs or feeds where possible. Reduce reliance on JavaScript-heavy rendering.
The shift is less about abandoning SEO and more about adding a “citation layer” across everything.
How Small & Medium Businesses Can Handle This
If you are a small business, you are probably overwhelmed, especially considering the points that imply brand mention on highly trusted sources being necessary for LLMs.
But you don’t need a big budget, start with this,
On-page: Turn customer FAQs into site FAQs. Add “last updated” stamps. Write short summaries at the top of posts.
Off-page: Don’t chase global media. Target local press, trade associations, and niche blogs. These are credible for AIs too.
Technical: Use WordPress plugins (Yoast, RankMath) to automate schema and freshness signals. Don’t block AI bots in robots.txt.
Customer-driven content: Use real questions from sales calls or customer support as blog/Q&A topics. LLMs mimic human phrasing.
Agility edge: SMBs, you can update faster than big enterprises, which gives you a freshness advantage in AI retrieval.
This isn’t about reinventing the wheel. It’s simply about making your existing SEO work AI-friendly.
If it still feels overwhelming, just start with step one: make your content AI-parsable. Focus on doubling down with content that’s diverse, fresh, clean, and clear, written in a way that works for both human readers and AI bots.
The Bottom Line
SEO makes sure crawlers can find, index, and rank your content in search results.
GEO/LLMO makes sure AI systems can understand, retrieve, and cite your content in their generated answers.
They’re not rivals, they’re stacked. Some of the GEO methods may have already existed, but it was avoidable; not anymore.
If SEO made you visible in the age of blue links, GEO ensures you’re still visible in the age of AI answers. And the best part? You don’t need to throw away your SEO playbook. You just need to layer on top.