Generative Engine Optimisation (GEO): How to Get Cited by ChatGPT, Gemini and Perplexity

TL;DR What you will learn

• GEO optimises content so ChatGPT, Gemini, and Perplexity mention, quote, and link to your brand.
• It differs from SEO: you are optimising to be cited in an answer, not ranked in a list.
• Core tactics include clear factual statements, quotable stats, strong structure, entity clarity and third-party mentions.
• It suits Singapore marketers, SMEs, founders, and B2B teams whose buyers now research inside AI tools.
• The payoff is visibility and trust at the decision point, even when clicks to your site fall.
• GEO builds on good SEO fundamentals rather than replacing them.

What Generative Engine Optimisation Really Means

Let me clear up the confusion first, because there is a lot of hype floating around LinkedIn right now.

Generative engine optimisation is the practice of shaping your content and digital presence so that AI assistants like ChatGPT, Gemini and Perplexity cite you inside the answers they generate, rather than simply linking to you in a list of blue results.

From SEO to GEO

For years, the goal was to rank a page. You wanted to sit at position one on Google so a searcher clicked through to your site.

GEO changes the finish line. Instead of earning a click, you are trying to be the source the AI pulls from when it writes its answer for the user.

That shift matters because the AI often answers the question without the user ever visiting your website. So the metric we now watch is share of citation: how often your brand appears as a named source across the answers your customers are getting.

If you want a solid grounding before you go further, it helps to have a refresher on how modern SEO works, because GEO builds directly on those fundamentals rather than throwing them out.

How LLMs assemble answers

Here is the plain version of what happens under the hood.

An LLM like the one powering ChatGPT was trained on a huge slice of the web, so it already carries a rough model of your industry from that training data. But training data goes stale, so these tools now add live web grounding.

That live layer usually works through retrieval-augmented generation, often shortened to RAG. When you ask a question, the tool retrieves fresh pages from the web, ranks them, then synthesises a single answer and attaches citations to the sources it leaned on.

Perplexity does this most visibly, showing numbered sources beside every answer. ChatGPT search and Gemini follow the same broad pattern, retrieving and grounding before they respond.

Why this matters now in Singapore

Local adoption is not theoretical. More Singaporeans are opening ChatGPT or Perplexity to research a supplier before they ever touch Google, and Singapore’s National AI Strategy 2.0 signals that AI is being woven into everyday work and life here.

Picture a small accounting firm in Tanjong Pagar. A prospective client asks Perplexity to explain the difference between a private limited company and a sole proprietorship for tax purposes in Singapore.

Perplexity summarises three competitor explainer articles, cites those firms by name, and never mentions the Tanjong Pagar firm. Those enquiries quietly go elsewhere, and the firm never sees them in its analytics.

The important point is that GEO complements your SEO; it does not replace it. The same well-structured, trustworthy content that ranks on Google is also what these AI engines prefer to retrieve and cite, so you are extending your existing work, not starting over.

How ChatGPT, Gemini and Perplexity Choose What to Cite

Before you can earn citations, you need to understand that each engine draws from a different well. Generative engine optimisation is not one game but three overlapping ones, and the source each engine trusts shapes what you should prioritise.

Let me show you how they differ, then walk through a real test you can run yourself.

ChatGPT and its Web Search Layer

When ChatGPT answers a live query, it grounds its response using a live web search layer (historically Bing-backed) rather than pure model memory.

That matters because it favours pages Bing already ranks and trusts: authoritative domains, clean headings, and content that answers the question directly near the top.

In my experience, if your page buries the answer under three paragraphs of preamble, ChatGPT tends to skip it in favour of a competitor who leads with the point. Structure is not optional here.

Google Gemini and AI Overviews

Gemini and the AI Overviews you see at the top of Google lean on Google’s own index, which means everything you already know about SEO still applies.

E-E-A-T signals carry real weight: author expertise, first-hand experience, and clear organisational trust cues all feed into whether Google feels comfortable summarising your page.

Structured data helps too. If you mark up an FAQ, a how-to, or an article with schema, you are handing Gemini a cleaner, more extractable version of your content, which raises your odds of being pulled into an overview.

Perplexity as a Citation-First Engine

Perplexity is my favourite testing ground because it wears its sources on its sleeve. Every claim comes with a visible numbered footnote linking back to the page it drew from.

That transparency makes it the fastest way to check whether your GEO work is landing. You do not have to guess; you can see exactly which URLs Perplexity chose and why.

Here is a practical test. Imagine a marketer at a Singapore fintech startup typing the same prompt, “Is PayNow safe for business use?”, into all three engines.

• ChatGPT might cite a MAS-linked explainer or a well-structured bank page, whatever Bing ranks highly.
• Gemini could surface an Association of Banks in Singapore resource or a page with strong author credentials and schema.
• Perplexity will footnote a mix, often pulling a local guide or a recognised financial site, and show you each source by name.

Run that comparison and a pattern emerges. The pages that get cited across all three share the same qualities: clarity, so the answer is easy to lift; authority, so the engine trusts the source; freshness, so the content is current; and extractability, so the machine can quote a self-contained passage.

Track those citations over time and treat them as one signal among the SEO KPIs worth tracking alongside citations, because a mention in an AI answer only pays off when it drives qualified traffic and trust.

Building Content That Language Models Want to Quote

When I audit content for generative engine optimisation, I keep asking one question: can a model lift a clean, self-contained sentence from this page without reading the rest? If the answer is no, the page rarely gets cited, no matter how good the writing feels to a human.

The good news is that everything I know about clear copywriting carries straight over into GEO. The habits that make a paragraph persuasive also make it extractable.

Answer-First Structure

Lead every section with a direct answer before you elaborate. Language models scan for a sentence that resolves the query on its own, then move on.

I saw this play out with a home-grown Singapore skincare brand we advised. Their old blog opened posts with brand storytelling and personal anecdotes before ever defining the ingredient in question.

We rewrote each article to start with a plain “What is” block. So a post on niacinamide now opened with a single sentence: “Niacinamide is a form of vitamin B3 that helps strengthen the skin barrier and reduce visible pores.”

Within a couple of months, they started appearing as a cited source in Perplexity answers for ingredient queries. The change was not more content. It was putting the liftable answer first.

Chunking and Semantic Headings

Models read structure, not just words. Descriptive headings act as labels that tell an LLM exactly what sits underneath.

Instead of a vague H2 like “More on ingredients”, the skincare brand used “How niacinamide differs from retinol” and “Is niacinamide safe for sensitive skin?”. Each heading maps to a real question someone types.

Under each heading, keep paragraphs short and include a clean definition sentence the model can isolate. If a paragraph carries three ideas welded together, no engine can quote it cleanly. This is the same discipline behind good on-page SEO, and if you want a fuller checklist, I would point you to Equinet’s content best practices for structuring pages.

Original Data and Quotable Statements

Models strongly prefer to cite content that says something no one else has said. Proprietary numbers, named frameworks, and crisp definitions all give an engine a reason to attribute the source to you.

You do not need a research budget to do this. The skincare brand ran a simple customer survey and published findings like the share of buyers who reported irritation when switching serum, framed as their own data.

• Publish a proprietary statistic from your own sales, survey, or usage data.
• Name your process or framework so it becomes a citable term.
• Write one-sentence definitions that stand alone without surrounding context.

Where this really connects for me is understanding the difference between copywriting and content writing: copywriting sharpens the quotable line, content writing supplies the depth that earns the model’s trust. GEO needs both working together.

Earning Authority and Trust Signals That AI Engines Reward

When I audit a client’s content for generative engine optimisation, the first thing I check is not keywords. It is whether the AI engine has any reason to trust the source over the thousands of anonymous blogs saying the same thing.

Large language models are trained to favour sources that show demonstrable expertise and reputation. If you want ChatGPT, Gemini or Perplexity to cite you, you need to look like an authority the way a real expert would recognise one.

E-E-A-T Signals Adapted for AI

You have probably heard of E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) from Google’s guidelines. The same logic applies here, just filtered through how machines read the web.

The strongest signal I keep seeing rewarded is a real, named author with verifiable credentials. An article by “Admin” carries almost no weight next to one written by a named specialist whose background you can actually check.

Take a licensed financial adviser in Singapore. If their content is written under a named author whose MAS-regulated status and qualifications are stated clearly, and that status can be cross-referenced against the MAS Financial Institutions Directory, an AI engine has a concrete reason to treat that page as trustworthy. An anonymous personal finance blog making identical claims does not get that benefit.

So the practical trust signals I focus on are:

• Author bios with real names, qualifications, and links to professional profiles
• Citations to reputable sources, and being cited by them in return
• Consistent brand mentions across the web, not just on your own site
• Third-party validation like reviews, directory listings, and industry recognition

Building Entity Recognition Across the Web

AI engines cite entities they recognise, and recognition is built off your own site, not just on it. The more your brand and your experts are referenced by trusted places, the more confidently an engine associates you with your topic.

For a Singapore business, that means being listed in credible local directories, being a member of relevant industry associations, and ideally being referenced by publications like The Straits Times or Tech in Asia. A mention in a respected local title does more for your entity strength than a dozen self-published posts.

This is also why I tell clients to keep their NAP (Name, Address, Phone number) consistent everywhere it appears. If your business name is spelled three different ways across your Google Business Profile, LinkedIn, and industry listings, you fragment the entity data engines rely on to connect the dots.

If you are hiring outside help for this, the same scrutiny you would apply when learning how to evaluate a digital marketing agency’s credibility is exactly the scrutiny an AI engine applies to you.

Build the trust signals a discerning human would look for, and the machines tend to follow.

Technical Foundations for AI Crawlers and Structured Data

You can write brilliant, quotable content, but if the AI engines cannot crawl or parse it, none of that effort feeds their answers. The technical layer of generative engine optimisation is where a lot of Singapore businesses quietly lose citations they should be winning.

Letting AI crawlers in

The first decision sits in your robots.txt file: do you allow the bots that power these engines to read your site?

The main ones to know are GPTBot (OpenAI, which feeds ChatGPT), Google-Extended (which governs whether your content trains and informs Gemini), and PerplexityBot (Perplexity’s crawler). You can allow or block each one individually.

Blocking them protects your content from being used, but it also removes you from consideration when someone asks an AI a question you could have answered. For most businesses chasing visibility, the trade-off favours allowing them.

Here is the nuance for a Singapore retailer. If you run a fashion or electronics store selling on Shopee and your own Shopify site, you probably want your product and buying-guide pages open to these crawlers, while still keeping account pages, checkout flows, and any pages carrying personal data behind a block. That last point matters for PDPA: never expose customer names, order details, or contact data on publicly crawlable pages just because it helps SEO. Crawlability should never override data protection.

Structured data and schema markup

Once the bots are in, structured data helps them understand what your page actually means rather than guessing from raw text.

Schema markup is a standardised vocabulary you add to your HTML so engines can reliably identify a product, a price, a review, an FAQ, an article, or your organisation details. The most useful types for GEO are:

• FAQ schema for question-and-answer blocks
• Article schema for blog posts and guides
• Organisation schema to confirm who you are and your credibility signals
• Product schema for e-commerce listings with attributes like availability

Picture that Shopee-and-own-site retailer again. On their own product pages, they add Product schema (price, stock status, ratings) and FAQ schema answering common questions like “Is this HDB-friendly?” or “Does it ship the same day within Singapore?”. When Gemini generates an AI Overview for “best affordable standing desk Singapore”, clean Product and FAQ markup makes their page far easier to pull into that answer than a competitor relying on unstructured text.

Clean, fast, accessible pages

The last piece is the boring plumbing that quietly decides whether extraction succeeds.

Semantic HTML, meaning proper heading hierarchy, real lists, and descriptive markup rather than a wall of divs, gives crawlers clear signposts to lift the right passage. Fast-loading, accessible pages get crawled more thoroughly and re-crawled more often, so your updates surface sooner.

If you are not sure where your setup stands, run it through some free SEO tools to audit your technical setup before you touch a single line of schema. Fix the crawl and speed basics first, then layer structured data on a foundation that actually holds.

Optimising for Conversational and Long-Tail Prompts

When I run generative engine optimisation projects, the first mindset shift I ask clients to make is this: people no longer type three keywords into a box. They talk.

From Keywords to Full Questions

Someone looking for a workspace does not search “co-working Singapore”. They ask ChatGPT something like “affordable co-working space near MRT for a two-person startup” and then follow up with “which one has meeting rooms and 24-hour access?”

That is a conversation, not a query. And AI engines answer the whole thread, pulling from sources that address each part clearly.

So your job is to map content to natural-language prompts and their follow-ups. I list out how a real customer would phrase their question, then the second and third questions they would likely ask after that.

A Singapore Co-Working Example

Take a co-working operator with a space near Paya Lebar MRT. Instead of one thin “our spaces” page, I would build a cluster that answers the journey end to end.

• A location page answering “co-working spaces near which MRT lines” with actual station names and walking times
• A pricing page answering “how much does a hot desk cost for a small startup” with plan tiers described qualitatively
• An amenities page answering “does the space have meeting rooms, printing and 24-hour access”
• A comparison page answering “co-working versus a serviced office for a two-person team”

Each page tackles one facet: location, price, amenities. Together they cover the variations an engine might be asked, so it keeps returning to that domain as the reliable source.

The key takeaway: comprehensive topic clusters that answer the full customer journey earn far more citations than a single overstuffed page.

Prompt Research Still Starts With Keywords

Here is what surprises people. Keyword research has not died; it has become prompt research.

Your seed topics still come from the same place. I take a seed like “co-working space Singapore” and expand it into question sets using the best keyword research tools to find prompt topics, then rewrite each keyword phrase as a real question a founder would ask.

The “people also ask” and related-question data you already use are gold for this. They reveal the follow-up prompts you need to answer on the page itself.

I usually add a short “related questions” section near the bottom of each page, then internally link it to the sibling pages in the cluster. That linking reinforces topical authority and tells both Google and generative engines that your site covers the subject thoroughly, not just in passing.

Measuring GEO Performance and Tracking AI Citations

Here is the uncomfortable truth about generative engine optimisation: the reporting is messier than anything you are used to in classic SEO. AI answers do not always link out, and when they do, the referral data is patchy at best.

So you have to build your own measurement habit. Let me walk you through the three methods I rely on, using a Singapore B2B software firm as our running example.

Manual Prompt Testing

The most reliable signal is still the one you check yourself. Pick 15 to 20 prompts a real buyer might type, then run them across ChatGPT, Gemini and Perplexity on a fixed schedule, say the first Monday of each month.

For our B2B software firm selling HR payroll tools, prompts might include “best payroll software for Singapore SMEs” or “how to stay CPF compliant with payroll automation”. For each engine, log whether the brand is mentioned, whether it is cited with a link, and which competitors appear alongside.

Keep it simple in a spreadsheet. One row per prompt, one column per engine, and a note on how you were framed, because being named as “a strong option” reads very differently from a one-line dismissal.

Analytics and referral signals

Manual testing tells you about visibility. Analytics tells you what that visibility does to your traffic.

In GA4, filter your acquisition reports for referral sources like chat.openai.com, perplexity.ai, and sources tied to Gemini. These sessions are usually low in volume but often high in intent, since someone clicked through after an AI recommended you specifically.

If you are still finding your feet with the platform, our guide on how to use Google Analytics 4 for beginners covers the setup you need before any of this makes sense.

Watch assisted conversions too, not just last click. An AI answer might introduce our payroll firm to a finance manager who returns via a branded Google search a week later, so the AI touchpoint deserves credit even though it did not close the deal directly.

Share of citation as a KPI

To make this a real programme rather than a hobby, you need one headline number. I call it share of citation.

Share of citation is the percentage of your tracked prompts where your brand is named or linked, measured against how often your named competitors appear.

If our payroll firm shows up in 8 of 20 prompts on Perplexity while a rival shows up in 14, that gap is your action list. Track it monthly, and you can see whether new content or PR mentions actually move the needle.

Treat this like any other reporting metric, and decide early which numbers matter most so you do not drown in noise.

A Practical GEO Workflow for Singapore Businesses

Everything above only matters if you can turn it into something a lean team actually runs each month. So here is the repeatable generative engine optimisation workflow I use with small Singapore businesses that do not have a big agency retainer.

Let me anchor this in a realistic scenario. Say you run a 12-person accounting firm in Ubi that helps SMEs with GST and corporate tax filing. Nobody on your team has done GEO before, and outsourcing the whole thing feels expensive.

Upskill Instead of Outsourcing

Before touching content, I would send one or two staff for training. A Singaporean employee can tap their SkillsFuture Credit to offset course fees, and the SkillsFuture Enterprise Credit (the current scheme runs until 30 Nov 2026, after which a redesigned SFEC takes over; check current eligibility) to defray out-of-pocket training and transformation costs.

That means your junior marketer learns to run this in-house, and you stop paying a monthly retainer for work you could own.

Steps One to Six: The Actual Loop

Once someone on the team is equipped, run these stages in order:

1. Audit current AI visibility. Ask ChatGPT, Gemini, and Perplexity questions your clients would ask, like “which Singapore firm helps small businesses with GST filing”, and note whether you appear or get cited at all.
2. Research target prompts. Do not boil the ocean. Pick three to five priority prompts tied to real client pain, such as “how do I file GST in Singapore” or “penalties for late corporate tax filing IRAS”.
3. Build or rewrite answer-first content. For each prompt, create a page that answers the question in the first two sentences, then expands with specifics like ACRA deadlines and worked examples.
4. Add schema and technical fixes. Mark up FAQs and articles with structured data, and make sure the page loads fast and is crawlable, because AI systems cannot cite what they cannot read cleanly.
5. Strengthen authority signals. Get a named author byline, list professional credentials, and earn a mention or two from local business directories or partner sites.
6. Measure and iterate monthly. Re-run your prompts, track which pages now get referenced, and refine the ones that still get ignored.

The key is starting small. Three to five prompts done properly beat fifty pages nobody cites.

Sustaining the Effort

The reason most GEO attempts fizzle is that they run it once and stop. To keep momentum, plan your rewrites and new pages ahead using a guide to creating a content calendar, scheduling one or two prompt clusters per month.

Treat GEO as a monthly habit, not a one-off project, and a lean Singapore team can absolutely run it in-house.

Common GEO Mistakes and How to Avoid Them

I have watched plenty of teams rush into generative engine optimisation with the same bad habits they picked up from old-school SEO, and it rarely ends well.

The mistakes below are the ones I see most often, and each has a straightforward fix.

Thin AI content that adds nothing new

The single biggest trap is publishing thin, AI-generated content dressed up as thought leadership.

I once looked at a Singapore tourism operator that mass-produced dozens of generic articles about “top things to do in Singapore” using a large language model, with no original booking data, no local insight, and no real voice.

They saw almost no citations. The models had no reason to quote them because everything they published already existed, phrased slightly differently, on a hundred other sites.

If your content only rephrases what the model already knows, it will never be cited. Add first-hand experience, proprietary numbers, named examples, or a clear point of view.

Keyword stuffing and accuracy shortcuts

Keyword stuffing does not become acceptable just because you relabel it as GEO. Cramming “generative engine optimisation” into every second sentence makes your writing worse and signals low quality to the very systems you are trying to impress.

Factual accuracy matters even more here than in classic SEO. When an assistant cites you and the claim turns out to be wrong, that erodes the trust that made you citable in the first place.

I always insist on human editorial oversight before anything goes live. A person needs to check that statistics are real, that scheme names like SkillsFuture or Enterprise Singapore are described correctly, and that no claim is invented for effect.

There are real risks attached to skipping this. Under PDPA, publishing content that exposes customer personal data, even accidentally through an unedited AI draft, can land you in genuine trouble, and unverified claims can do lasting reputational damage.

Blocking crawlers and chasing every engine

Some teams block AI crawlers in robots.txt without understanding the trade-off. That is a valid choice if you want to protect your content, but do it deliberately, knowing you are opting out of citations, not by accident.

Finally, do not try to win every engine at once. If your buyers in Singapore lean on Perplexity for research or Gemini inside their Google workspace, focus there rather than spreading yourself thin.

Here is a quick sanity check before you publish anything:

• Does this add original value the model does not already have?
• Have I verified every fact, figure, and scheme name?
• Have I checked for any exposed personal data?
• Is the target engine one my audience actually uses?

Get these right and you avoid most of the pain I see teams walk into.

Here is the truth I keep coming back to: generative engine optimisation is not a replacement for SEO, it is the next layer of discoverability sitting on top of it.

The businesses that structure clear, authoritative, extractable content today are the ones that will own share of citation as AI search matures across ChatGPT, Gemini and Perplexity.

Think of it this way. Your Google rankings still matter, but a growing slice of your customers now ask an AI assistant first and never scroll a results page at all.

The workflow we walked through is not complicated. You publish content that answers real questions directly, you back claims with credible sources and named expertise, you structure pages so machines can lift a clean answer, and you measure whether the models actually cite you.

Authority, clarity and measurement win. Those three keep showing up whether I am helping a local B2B software firm or a Toa Payoh clinic get named in AI answers.

You do not need a big project to begin. This week, run a small prompt audit: type ten questions your customers actually ask into ChatGPT and Perplexity, note who gets cited, and honestly ask why it is not you yet.

That single exercise usually reveals the gaps faster than any tool.

If you want to build this capability in-house rather than outsourcing it forever, the fundamentals of on-page structure, intent and content quality are the same muscles GEO relies on. You can strengthen them through the WSQ Advanced SEO, AEO & GEO Course at Equinet Academy, which is SkillsFuture-supported for eligible Singaporeans and Singapore companies.

It is the logical next step because once your team can execute solid SEO, layering GEO on top becomes a natural extension, not a whole new discipline.

Start small, stay consistent, and turn AI answers into your next real growth channel.