The Marketer's Guide to ChatGPT Keywords/Prompts

A weird thing is happening in marketing right now.

Your buyer is still Googling, sure. But they’re also asking ChatGPT what tool to use, what vendor to trust, what “the best” option is. And sometimes they do it before they ever see a search results page. No ten blue links. No scrolling. Just one clean answer that feels… final.

That’s the shift.

And it’s why “keywords” are suddenly not just SEO keywords anymore. They’re prompts. They’re the exact questions people type into ChatGPT, Gemini, Claude, Perplexity, and now Google AI Overviews.

If you’re a marketing leader, an SEO pro, a GEO specialist, or you run an agency, you need a working playbook for those prompts. How to find them, how to write them, how to test them, and how to measure whether your brand is showing up inside the answer.

This is that playbook.

First, what we mean by ChatGPT keywords

In classic SEO, a keyword is a query you want to rank for in Google.

In AI search, a “keyword” is still a query, but it behaves differently:

It’s often longer and more specific.
It’s conversational.
It includes constraints like “for enterprise”, “in the UK”, “with SOC 2”, “for Shopify”, “under $500”.
It triggers an AI to synthesize an answer, not retrieve a list.

So when I say ChatGPT keywords/prompts, I mean the real user questions that cause AI engines to recommend brands, cite sources, or mention products.

And marketers now care about two outcomes:

Mention: Your brand/domain appears in the response text (including entities and cited source metadata).
Link/Citation: The AI cites your domain, references your content, or links to you directly.

Mentions build trust. Links drive measurable traffic. You usually want both.

SEO vs GEO, and why it matters (without the hype)

SEO is still important. But traditional SEO metrics do not fully capture what’s happening inside AI answers.

A brand can “rank” well and still not be named by AI engines. Or the opposite. A brand can be mediocre in organic rankings yet repeatedly cited in AI responses because it has the clearest explanation, the most cited study, the most direct comparison table, the most quotable definition.

That’s the core idea behind GEO, Generative Engine Optimization.

GEO is about optimizing brand presence within AI generated responses, beyond traditional SEO. Not just “where do we rank”, but “do we get selected”.

If that sounds fuzzy, it doesn’t have to be. You can measure it.

The measurement problem: you cannot optimize what you cannot reproduce

Here’s what makes AI visibility hard:

AI answers vary by region and language.
AI answers change over time.
AI answers differ across engines (ChatGPT vs Gemini vs Claude vs Perplexity vs Google AI Overviews).
Some engines browse live web. Some rely more on cached or model knowledge unless you force browsing.
A lot of teams test prompts manually, once, from one laptop, in one city, then screenshot the result and call it “tracking”.

That’s not tracking. That’s vibes.

What you want is consistent, localized, repeatable querying across engines, stored responses (not truncated), and scoring that separates mention vs link visibility. Ideally with history, competitor share, and exportable reporting.

This is basically what VisiScore.ai is built for. It quantifies brand visibility across generative AI engines using live queries, localized to a target country, and rolls it up into a proprietary prominence score across five engines.

And importantly, it’s transparent about the weighting:

70% brand mention
30% direct link/citation

It then categorizes results as:

Strong (70%+)
Moderate (40 to 69%)
Low (below 40%)

You do not need to use VisiScore to benefit from this guide. But you do need the mindset behind it: prompt visibility is measurable, and it should be measured like a serious channel.

For brands looking to enhance their visibility in AI-generated content, understanding how to get featured in platforms like ChatGPT can be crucial. There are specific strategies that can significantly improve your chances of being included in these AI-generated responses. This guide provides a comprehensive overview of those strategies – a must-read for any brand aiming to optimize its presence in the rapidly evolving digital landscape dominated by generative AI technologies.

Prompt intent buckets marketers should track

Most teams track prompts randomly. Whatever someone on the team thinks of. That’s how you end up tracking “what is GEO” instead of tracking the prompts that actually decide revenue.

A cleaner way is to organize prompts by intent. Here are the buckets that matter most.

1. Category discovery prompts (top of funnel, high leverage)

These are the “what should I even look at” prompts:

“Best [category] tools for [use case]”
“Top alternatives to [competitor]”
“What is the best [category] platform for a mid size team”
“Which [category] tool is best for enterprise”

Examples:

“Best generative engine optimization platforms”
“Top AI visibility tracking tools for marketing teams”
“Best tools to measure brand presence in ChatGPT and Google AI Overviews”

These prompts often produce shortlist style answers. If your brand is missing, you are invisible in the early decision stage.

2. Comparison prompts (mid funnel, decision shaping)

These tend to be more specific and more valuable:

“[Brand A] vs [Brand B]”
“Compare [3 to 5 brands] for [use case]”
“Which is better for [industry]”

Examples:

“VisiScore vs Profound for AI search visibility”
“Best AI Overview keyword tracking tools and how they compare”
“Which platform tracks ChatGPT, Gemini, Claude, and Perplexity visibility”

Comparisons are where positioning is won or lost because AI will summarize strengths and weaknesses. If the model describes you inaccurately, that’s a content and authority problem you can actually fix.

3. Requirements prompts (high intent, procurement energy)

This is the prompt type that sounds boring, but it’s where enterprise buyers live:

“SOC 2 compliant [category] tools”
“[Category] platform with API access”
“[Category] tool that supports multi workspace and agency reporting”
“[Category] tool for global localization”

If you have differentiators like live web enabled data, geo localization, enterprise APIs, or Excel exports, these prompts are the ones that should surface them.

4. Implementation prompts (post purchase influence, expansion)

People ask:

“How do I measure AI visibility for my brand”
“How to track mentions in ChatGPT”
“How to do GEO reporting for clients”
“How to find keywords that trigger Google AI Overviews”

If your brand helps solve these problems, being cited here can drive demos, but it can also drive retention and expansion because customers share those resources internally.

5. “Definition plus recommendation” prompts (sneaky commercial)

These look informational, but they often end with a “what should I use” hook.

“What is generative engine optimization and what tools help with it”
“What is AI search visibility and how do you measure it”

If your brand only chases “best tools” prompts, you miss these.

How to build your prompt list (without overthinking it)

You need enough prompts to cover reality, but not so many you drown. Start small, then expand.

A practical starter set for most brands is 30 to 60 prompts, broken across:

10 category discovery
10 comparisons (you vs key competitors)
10 requirements
10 implementation
5 to 20 industry specific or localized variants

If you’re an agency managing multiple clients, you replicate the framework per client and keep a shared library of prompt patterns.

To discover high-value prompts at scale, consider leveraging VisiScore’s resources which support 1 visibility scan with up to 10 tracked keywords/prompts (minimum 3), with unlimited competitor tracking, plus real time web enabled engine responses and global localization. This approach not only helps validate whether your brand is currently being selected but also provides insights into AB testing prompts which can significantly enhance your prompt strategy.

The prompt patterns that actually work for marketers

Marketers often write prompts like they’re talking to a friend. That’s fine for casual use. But if you want consistent, comparable outputs, you need prompts that reduce ambiguity.

Here are prompt templates I’ve seen work well across engines.

Pattern A: Shortlist prompt (forces selection)

Use when you want to see if you appear in the top picks.

Template: “List the top 7 [category] tools for [use case]. Include 1 to 2 sentence rationale for each. Prefer tools that [differentiator].”

Example: “List the top 7 platforms for tracking brand visibility in generative AI engines. Include 1 to 2 sentence rationale for each. Prefer tools that can track ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews.”

Pattern B: Buyer persona prompt (forces fit)

Template: “I’m a [role] at a [company type] in [country]. We need [requirements]. Recommend 3 options and explain tradeoffs.”

Example: “I’m a CMO at an enterprise SaaS company in Germany. We need a way to measure brand visibility in ChatGPT, Perplexity, and Google AI Overviews, localized to Germany, and export results for reporting. Recommend 3 options and explain tradeoffs.”

This tends to produce more realistic vendor selection language.

Pattern C: Comparison table prompt (forces structure)

Template: “Compare [Brand A], [Brand B], [Brand C] for [use case]. Create a table with: coverage, data freshness, localization, reporting/exports, pricing model, best fit.”

If your brand has strengths (like live web enabled queries, hyper localized tracking, native Excel exports), tables surface them quickly.

Bonus Tip: Evaluating Brand Visibility on ChatGPT

For marketers looking to gauge their brand's visibility on platforms like ChatGPT, it's crucial to have the right tools at your disposal. You can start by checking your brand's visibility on ChatGPT with specialized tools. This not only helps in understanding where you stand but also aids in making informed decisions based on the data gathered from such evaluations.

Pattern D: Citation forward prompt (forces sources)

This is crucial when you care about links, not just mentions.

Template: “Answer the question: [question]. Use citations and link to sources.”

Example: “What are the best practices for measuring brand visibility inside AI generated answers. Use citations and link to sources.”

On engines that support citations well, this is where link share becomes visible.

Pattern E: “Live web” prompt (forces freshness when supported)

Some engines can browse live web, but you often need to be explicit.

Template: “Use live web browsing. Only use sources from the last 12 months. Provide links.”

VisiScore also supports live web enabled queries with precise URLs for fresh data, which matters because it reduces the gap between what the model “knows” and what is currently true.

Localized prompts are not optional anymore

AI answers vary by region. Not slightly. Sometimes wildly.

A UK prompt for “best payroll software” can produce a completely different shortlist than the same prompt in the US. Same for legal tools, banks, healthcare, even marketing agencies.

So if you’re an international brand, you need to track prompts by country, not just globally.

VisiScore’s approach here is worth copying conceptually: parameter based global localization that constrains every query to a specified country, and stores the full response for auditability.

Because if you show your CEO a screenshot from New York, and your pipeline is in Singapore, you’re not measuring the same reality.

Scoring: stop counting only “mentions”

Mentions are good. But not all mentions are equal.

A weak mention looks like: “Other tools include X, Y, Z.” No context. No link. No recommendation language.

A strong mention looks like: “For tracking brand prominence across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews, VisiScore.ai provides localized, live web enabled responses and exports reporting.”

And citations matter because they are the bridge between AI answers and traffic.

That’s why a blended scoring model like 70% mention and 30% direct link is reasonable. It forces you to care about being named and being sourced.

If you want a simple internal benchmark, use something like:

If we are not mentioned, we are not in the consideration set.
If we are mentioned but not cited, we are trusted but not driving clicks.
If we are cited, we have a path to measurable acquisition.

A simple workflow to operationalize GEO prompts

This is what I’d run as a marketing team, even a small one.

Step 1: Pick 10 prompts that map to revenue

Not definitions. Not “what is”. Pick the prompts that resemble buying behavior.

Step 2: Track across five engines

At minimum: ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. The outputs differ, and your visibility will differ too.

Step 3: Localize to your target market

Track US separately from UK separately from AU, etc. If you sell internationally, this becomes a matrix, yes. But it’s the real one.

Step 4: Store full responses for retroactive audits

You need to answer "when did we start getting cited" and "what changed". This requires historical storage, not just a score.

Step 5: Add competitor tracking

Unlimited competitor tracking is ideal because you will learn faster by watching who wins each prompt type, and then reverse engineering what content gets them cited.

Step 6: Report it like a channel

Weekly or monthly, depending on how fast your category moves. Tie it to content releases, digital PR, product launches, and site changes.

VisiScore's scan scheduling and date comparison tools are basically built for this ROI loop. Run a scan, ship content, scan again, compare.

What to do when you are not showing up

This is the part everyone wants. The "ok but how do I fix it".

You fix it the same way AI engines decide answers. They need sources that are clear, specific, consistent, and easy to cite.

A few moves that tend to increase citations and mentions over time:

1. Write the pages that AI wants to quote

Focus on content types that AI engines are most likely to reference and summarize:

Comparison pages
"Best for" pages
Implementation guides
Definitions with examples
Pricing and packaging clarity — if your pricing model is unusual, explain it plainly. VisiScore's per keyword/prompt model with scan frequency pricing is a good example of something an AI can summarize cleanly.

2. Make your claims verifiable

Avoid vague "leading platform" language. Use specifics that AI engines can extract and repeat with confidence:

Coverage across engines
Localization method
Data freshness
Export formats — Excel matters in real enterprises
What you do not track — for example, VisiScore does not track traditional organic rankings, sentiment analysis, or social mentions, and being explicit about this builds trust

3. Publish assets that show methodology

AI engines tend to trust pages that explain how something is measured. If you have a score, explain weighting and categories in plain English. If you use APIs, say that. If you store full responses, say that.

4. Earn third-party mentions

AI models and AI browsing systems still lean on the broader web. If credible sites mention you in the context of your category, that helps you become "selectable".

The marketer’s prompt library (copy and paste)

A handful of ready to use prompts you can start tracking today.

“What are the best tools to measure brand visibility in ChatGPT”
“List the top 10 generative engine optimization platforms and what each is best for”
“What tools track visibility across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews”
“How do I measure share of voice in AI search results”
“What is GEO and how is it different from SEO. Recommend tools to track it”
“Compare [Your Brand] vs [Competitor] for AI search visibility tracking. Give a table”
“Best way to find keywords that trigger Google AI Overviews”
“How do agencies report AI visibility to clients. Include templates and tools”
“What is the best AI visibility tracking solution for an enterprise marketing team with international markets”
“Recommend a platform for hyper localized AI engine response tracking by country”

Track those. Then start adding industry modifiers. “for healthcare”, “for fintech”, “for ecommerce”, “for B2B SaaS”. The prompt list grows fast, but in a good way.

Picking scan frequency (monthly vs weekly vs daily)

Most teams do not need daily scans for everything. But you might need daily scans for a handful of prompts during:

a product launch
a rebrand
a major algorithm change (Google AI Overviews expansions, for instance)
a competitive moment in your market

A utility style model, priced per keyword/prompt by scan frequency, maps well to this reality. VisiScore’s PRO plan pricing is a clean example of how teams budget this:

Monthly scans: $0.75 per keyword/prompt
Weekly scans: $2.50 per keyword/prompt
Daily scans: $10.00 per keyword/prompt

The idea is not “scan everything daily”. The idea is “scan what matters at the tempo the business needs”.

One last thing: treat AI visibility like reputation, not a hack

If you take nothing else from this.

You do not “game” ChatGPT prompts the way people gamed SEO in 2014. You build a brand and a content footprint that AI engines can confidently summarize and cite. You measure whether it is happening. You compare against competitors. You iterate.

GEO is starting to feel like SEO did a decade ago. Not because it’s trendy. Because discovery is shifting.

So start with 10 prompts. Track them across engines. Localize them to your real markets. Store the outputs. Measure mentions and citations separately.

Then do the basic marketer thing. Publish what the market needs, make it easy to cite, and keep score.

FAQs (Frequently Asked Questions)

The shift in marketing is that buyers are increasingly asking AI engines like ChatGPT for recommendations, vendor trustworthiness, and best options before even seeing traditional search results. This changes keywords from classic SEO terms into conversational prompts that trigger synthesized AI answers rather than lists of links.

How do ChatGPT keywords differ from traditional SEO keywords?

ChatGPT keywords, or prompts, are typically longer, more specific, and conversational. They often include constraints such as 'for enterprise', 'in the UK', or 'under $500'. Unlike traditional SEO keywords that aim to rank in Google search results, these prompts trigger AI engines to synthesize a direct answer, often recommending brands or citing sources.

What is Generative Engine Optimization (GEO) and why does it matter?

GEO refers to optimizing brand presence within AI-generated responses across generative engines like ChatGPT, Gemini, Claude, and others. Unlike traditional SEO which focuses on ranking in organic search results, GEO focuses on whether a brand is mentioned or cited directly by AI answers. This matters because a brand can perform well in GEO even if its organic rankings are mediocre.

How can marketers measure their brand's visibility within AI-generated answers?

Measuring brand visibility in AI-generated answers requires consistent, localized, repeatable querying across different AI engines with stored responses and scoring systems that differentiate between mentions and direct links or citations. Tools like VisiScore.ai provide a proprietary prominence score based on brand mentions (70%) and direct citations (30%), categorizing visibility as strong, moderate, or low.

Why is prompt intent important for tracking and optimizing AI search presence?

Prompt intent helps marketers organize queries by their purpose rather than tracking random prompts. Focusing on intent buckets—such as category discovery prompts that represent top-of-funnel searches—allows teams to prioritize prompts that influence revenue decisions and optimize content accordingly for better AI visibility.

What types of category discovery prompts should marketers focus on for better AI visibility?

Marketers should focus on prompts like 'Best [category] tools for [use case]', 'Top alternatives to [competitor]', 'What is the best [category] platform for a mid size team', or 'Which [category] tool is best for enterprise.' Examples include 'Best generative engine optimization platforms' or 'Top AI visibility tracking tools for marketing teams.' These high-leverage prompts help brands appear prominently in AI-generated recommendations.