Prompt Optimization and Ethical Influence on AI Outputs

Understanding User Prompts in the Age of AI Search

The way people search for information is shifting from typing keywords into a browser to asking full-fledged questions in conversational AI tools. A few years ago, a typical query might have been “best running shoes 2022” —terse and keyword-based.

Today, users are more likely to ask an AI assistant something like: “Which running shoes are best for long-distance runners with flat feet under $150?” This represents a fundamental change in search behavior. People treat AI assistants like ChatGPT, Google’s Bard (powered by Gemini ), or Bing Chat as if they were human experts, phrasing queries in natural language and expecting direct, well-reasoned answers ( ^[1] ) ( ^[2] ).

The implication for marketers is clear:

Optimizing solely for terse keywords is no longer enough. We must understand and anticipate the actual questions our audience might pose to an AI engine about our industry, products, or services. This evolution is happening rapidly.

By mid-2024, roughly one in ten U.S. internet users were already turning to generative AI first for online search ( ^[3] ). Global studies show ChatGPT and Google’s Gemini (the model underpinning Bard and Search Generative Experience) dominate this emerging AI search market with 78% of traffic ( ^[4] ). Bing Chat and newcomers like Perplexity.ai constitute much of the rest ( ^[5] ).

In practical terms, that means tens of millions of people are asking ChatGPT or similar tools for advice and answers instead of (or before) using Google. A 2024 Statista survey estimated 13 million Americans were using generative AI as their primary search tool in 2023, and projections suggest this could soar to over 90 million by 2027 ( ^[6] ) ( ^[7] ).

Gartner analysts even predict traditional search volume may drop 25% by 2026, with organic traffic potentially decreasing by more than 50% as consumers embrace AI-powered search ( ^[6] ). This trend is illustrated by the AI platform traffic share in 2024, where ChatGPT and Google’s AI account for the lion’s share.

These numbers underscore a pivotal point: AI is becoming the new gatekeeper between customers and content ( ^[8] ) ( ^[9] ).

Users are delighted by getting a single, synthesized answer without browsing multiple sites ( ^[10] ). But for marketers, it’s disconcerting – the AI now decides which brands or pages “deserve” a mention in its answer ( ^[8] ).

To thrive in this environment, we must research how our target audience phrases questions to AI and ensure our content aligns with those queries. Start by considering the intents and language your customers use. For example, a consumer might ask: “ How can I fix acne breakouts without harsh chemicals? ” if you’re in the skincare industry, or “ What’s the best CRM software for a mid-size e-commerce business? ” if you offer B2B software. Notice how these resemble the natural, specific questions one would ask a knowledgeable person, rather than stilted keyword strings.

Brainstorm (or better, research ) the common questions in your niche: 

1. Product comparison prompts: “Which [product] is best for [specific need] ?” (e.g., “Which laptop is best for graphic design under $1000?” ). 
2. Problem-solution prompts: “How do I solve [problem] without [undesirable solution] ?” (e.g., “How to unclog a drain without harsh chemicals?” ). 
3. Alternative prompts: “What can I use instead of X ?” (e.g., “What are good alternatives to Photoshop for beginners?” ). 
4. Best-of lists and tips: “What are the top N [items] for [goal] ?” (e.g., “What are the top 5 project management tips for remote teams?” ).

One way to gather these prompts is to consult your existing data and customer interactions. Talk to sales and support teams about the questions they hear most. Analyze community forums, Reddit, Quora, or social media groups in your industry – these often reveal exactly how real users phrase their problems and product searches.

For instance, on Reddit a user might ask, “Anyone have recommendations for a vitamin that boosts energy but doesn’t disrupt sleep?” (as noted in a recent marketing analysis of ChatGPT’s shopping feature) ( ^[11] ). Such phrasing is gold for your content strategy, because it tells you the precise language and criteria (energy boost, no sleep issues) that matter to potential customers. It’s also instructive to study how AI itself breaks down queries.

Unlike Google’s traditional approach of matching keywords to pages, ChatGPT’s web-enabled search (ChatGPT with browsing/SearchGPT) and similar AI systems decompose a complex question into sub-queries and scour multiple sources before synthesizing an answer ( ^[12] ) ( ^[13] ).

For example, if a user asks: “I need a durable, colorful iPad case that isn’t too bulky,” a Google search might return shopping ads and a list of links, whereas ChatGPT will internally search for “durable colorful iPad case,” “best iPad case not bulky,” etc., and pull data from forums, blogs, product pages, and expert reviews – then summarize everything into a single recommendation ( ^[14] ) ( ^[15] ).

AI has the ability to use not just official product info, but also unstructured content like forum discussions and Q&As, because it “reads” entire pages and understands context, rather than simply indexing keywords. In fact, ChatGPT’s search has been observed to rely heavily on forum posts, Reddit threads, and long-form articles when answering certain queries, even more so than standard search engines ( ^[16] ) ( ^[17] ). This means user-generated content and the broader conversation about your brand online (reviews, discussions) can directly influence AI outputs.

Crucially, generative AI often prefers answering questions rather than just listing websites. Data from Baidu (China’s largest search engine) illustrates this preference on an international scale. In 2024, Baidu reported that about 18% of all searches on its engine were being answered directly by its AI “Smart Answer” feature, which is akin to Google’s featured snippets or SGE ( ^[18] ). These AI answers were triggered most by question-based queries – in one analysis, nearly 69% of searches phrased as a question (e.g. starting with “what is…”, “how to…”) returned an AI-generated answer, whereas generic product-name searches triggered AI answers less than 2% of the time ( ^[19] ) ( ^[20] ).

In other words, both Western and Eastern search platforms are converging on the idea that if the user asks a clear question, an AI-crafted answer should appear. Marketers therefore need to meet users in their question format. If your content doesn’t address the questions people are asking, it’s invisible to these answer engines.

To optimize for the AI era, empathize with your audience’s questioning style. In workshops or brainstorming sessions, put yourself in the customer’s shoes and formulate the kinds of detailed questions they might ask at different stages of the buying journey:

1. Early research: “What are the benefits of solar panels for a small home?” (Top-of-funnel informational query).
2. Mid consideration: “How does [YourCompany]’s solar panel efficiency compare to [Competitor]?” (Comparison query).
3. Decision/validation: “Is [YourProduct] suitable for coastal climates?” or “Has anyone used [YourProduct] for more than 5 years – how reliable is it?” (Highly specific, often looking for user experiences or case studies).

By understanding these prompts, you can craft content that directly answers them, increasing the likelihood that an AI will surface your information in response. An AI cannot magically recommend your product or content if it doesn’t find relevant text that addresses the user’s query.

As one LinkedIn SEO expert succinctly put it, if you want ChatGPT to include your brand in its answers, you must “create detailed, structured content that answers customer questions” in the first place ( ^[21] ). This is the essence of Generative Engine Optimization: aligning your content to the questions and language of real users.

Before moving on, here’s a practical exercise: take two minutes to ask ChatGPT (or Bing Chat, Bard, etc.) what it knows about your product or company. Simply type a question like, “What does [Your Company] do?” or “Is [Your Product] good for [use case]?” The answer might surprise you.

If the AI provides an incomplete or outdated description, or worse – doesn’t mention you at all – it’s a glaring sign that your current content isn’t sufficiently aligned with the queries or lacks the depth for the AI to latch onto. Many marketers have tried this experiment and discovered gaps; as one content strategist noted, “You might be surprised to find what’s missing… then you can get to work filling those gaps.” ( ^[22] ). Use this as a starting benchmark for your prompt optimization efforts.

Incorporating Likely Questions Into Your Content

Once you’ve identified the common questions and phrasing your audience uses, the next step is to embed those questions (and their answers) within your content. This approach isn’t entirely new – FAQ pages and Q&A content have been SEO staples for years – but it takes on renewed importance in the age of LLMs.

Generative AI models literally learn from text patterns, so phrasing content in a natural, question-and-answer format can make it easier for an AI to recognize that your page contains a relevant solution to a user’s prompt. In fact, early research indicates that content structured around FAQs and question-based headings is highly favored by AI answer engines like Google’s SGE, ChatGPT/Bing, and Perplexity ( ^[23] ) ( ^[24] ).

How should you incorporate questions in practice? Here are several tactics: 

Use natural language questions as headings (H2/H3): Instead of opaque headings like “Benefits of Product X”, reframe them as the questions users ask. For example, a heading on a footwear e-commerce site might be “How do I choose the right running shoes?”, or on a cybersecurity B2B site: “What’s the best way to prevent phishing attacks in a small business?”. Following the heading, immediately provide a concise answer or summary. This mirrors the structure of a Q&A, which models find easy to parse. According to SEO experiments, “using clear, hierarchical headings (H1, H2, H3) that match natural questions” greatly aids LLMs in extracting relevant info ( ^[25] ). Essentially, you’re making your content’s intent explicit: “here is a question, and below is the answer.” This format not only helps AI; it also improves human readability by directly addressing the reader’s likely queries. 

Develop dedicated FAQ sections or pages: Frequently Asked Questions sections shine in AI contexts. They bundle together many concise Q&A pairs, which are exactly the nuggets an LLM loves to deliver. A 2025 study found that FAQs are among the most cited page types in AI-generated answers, with one analysis noting 82.5% of AI overview citations on Google linked to “deep” content pages like FAQs (pages not just homepages, often two clicks from the main page) ( ^[24] ). This makes sense – an FAQ often directly answers specific queries with minimal fluff, which is ideal for an AI snippet. Ensure your FAQ answers are standalone useful : even out of context, an answer should make sense. For instance, an FAQ entry might be: “ Q: Can I use [Product] if I have sensitive skin? A: Yes. [Product] is formulated without common irritants, and in a clinical trial with 100 people with sensitive skin, 95% reported no adverse reactions. Its gentle ingredients like aloe vera are specifically chosen for sensitive skin comfort.” This answer is factual, contains a stat (which AI see as evidence of credibility), and directly addresses the question. An LLM like ChatGPT could easily incorporate that into a response about the product’s suitability. 

Embed likely user questions at strategic points: Consider adding a “Q&A” or “People also ask” style callout in blog posts or guides. For example, in a long-form article about running shoes, you could interject a callout box: “ Q: What’s the difference between stability and neutral running shoes? A: Stability shoes have features to control motion for runners who overpronate (excessive inward foot roll), whereas neutral shoes are for those with a normal gait. Depending on your pronation, one will be better to prevent injury.” These inline Q&As break up content and directly mirror the conversational queries someone might pose to an AI or voice assistant. Notably, Google’s own SGE often bolds or highlights text in its AI overviews that directly answers a presumed question, so formatting your content to answer questions clearly can help both AI and traditional snippet selection. 

Leverage schema markup (where applicable): While the AI models themselves don’t read schema markup per se, using structured data like FAQPage schema can indirectly help by making your content eligible for rich results on search and possibly feeding Google’s knowledge graphs. Google’s AI overview might draw from sources it trusts in its index – having schema-validated FAQs could be a quality signal. Even beyond Google, structured content is easier for any algorithm to digest. One agency noted that using semantic HTML and schema helped LLMs identify relevant content pieces more accurately ( ^[26] ). It’s not a silver bullet, but it aligns with the strategy of being machine-friendly. 

Write in a conversational yet concise tone: When answering the questions in content, aim for a tone that is confident and straightforward, as if an expert is speaking to the user. Generative models often pick up on text that sounds authoritative and clear. They may even quote or closely paraphrase parts of your text in their answer. For instance, if your blog says, “In summary, if you have flat feet, stability running shoes are generally the best choice as they provide arch support and reduce overpronation,” an LLM might directly use that phrasing because it succinctly answers a likely user query (“What kind of shoes are best for flat feet?”). Keep sentences reasonably short and focused; avoid burying the answer in fluff or subordinate clauses. The AI might not use your entire paragraph—often it will extract the one sentence or two that seem to answer the question best ( ^[25] ). So make that golden sentence stand out. Some SEO experts even suggest using phrases like “In summary, … ” or “ The key takeaway is, … ” just before your answer statement ( ^[25] ). This acts as a linguistic signal that what follows is an important conclusion, which can prompt an LLM to latch onto it. 

Provide context and definitions proactively: Users often ask “What is X?” or “How does X work?” If you have technical or niche terms related to your business, consider adding quick definitions. For example, a fintech site might include, “ Q: What is PSD2 compliance? A: It refers to the Payment Services Directive 2, an EU regulation that requires stronger verification for online payments, affecting how fintech apps handle user authentication.” If your content pre-empts a term explanation, an AI might quote your definition when a user asks that general question. This not only gets you cited but also positions you as an authority. Even within a broader article, a parenthetical defining a term can be useful (e.g., “Our solar panels use PERC technology (Passivated Emitter and Rear Cell, a design that increases efficiency by reflecting unused light back into the cell)….”). A well-phrased definition can end up as the snippet an AI gives for “What is PERC solar panel?”.

To see these tactics in action, consider the following real-world example. A marketer at a SaaS company observed that people often ask ChatGPT questions like, “Which subscription management tool is best for a growing SaaS startup?” In response, the company published a guide titled “Which Subscription Management Software is Best for Your SaaS Business?”. The guide’s subsections included Q-style headings like “ How do I choose the right subscription management tool? ” and “ What features matter most in subscription billing for startups? ”, each followed by concise answers ( ^[27] ).

They also created a comparison table of top tools, and an FAQ at the end addressing queries like “Does [YourProduct] integrate with Stripe?” and “Is [YourProduct] compliant with EU billing regulations?”.

The result: not only did this page rank well on traditional Google, but snippets of it were picked up by Bing Chat and Perplexity in answer to user questions, and even ChatGPT (with browsing) referenced its content when the question matched closely. By literally incorporating the language of user prompts into onsite content, you increase the chance that the AI’s answer will echo your content.

It’s worth noting that this strategy aligns with the older concept of Answer Engine Optimization (AEO) that many SEOs practiced for voice search and featured snippets. The twist now is that the answers might not send a click back to you (often the AI just uses your content without a click-through), but being mentioned or cited by the AI has its own branding and visibility value – something we’ll measure differently.

An AI like Perplexity will directly cite and link your page if used, which can drive some traffic ( ^[23] ). Google’s SGE shows your site name as a source in its AI summary, potentially increasing brand exposure even if clicks drop. ChatGPT with browsing will list sources (and a user might click those). So earning that citation or mention is the new win, analogous to getting the featured snippet position in classic SEO.

To maximize success: keep your content fresh and up-to-date while embedding these questions. AI models and search engines give preference to current information for many queries (we’ve seen product pages and up-to-date guides appearing more in AI answers lately) ( ^[28] ). Use indicators like dates or “Updated for 2025” in your content where relevant; one study suggested that visible freshness cues can increase an AI’s confidence in citing content ( ^[29] ).

And always ensure the answers you provide are accurate and truthful – don’t oversell or you risk the AI disregarding your promotional tone. For example, avoid answering “Which product is best?” with “Obviously, our product is the best of all!” on your own site – an AI is likely to either omit that as biased or pair it with more neutral info from elsewhere.

Instead, answer objectively, perhaps citing third-party evidence or differentiators, like, “ Why choose us over X? – We offer [unique benefit], while X lacks this feature (according to [Source]).”

In summary, structure your content to directly answer the real questions. By doing so, you not only help your human readers (who appreciate clear answers), but you essentially hand-deliver to AI models the building blocks they need to include your insights in their outputs. The result is a double win: an improved user experience on your site and a greater likelihood of being part of AI-generated answers on external platforms.

Avoiding Manipulative “Black-Hat” Tactics

Whenever a new technology or platform emerges, some will try to “game” it for quick gains – generative AI is no exception. We’re already seeing chatter about “black-hat GEO” or shady tactics to manipulate LLM outputs. As tempting as it may be to attempt shortcuts, it’s critical to steer clear of manipulative strategies that could backfire.

In the SEO world, trying to trick Google with keyword stuffing, cloaked pages, or link farms ultimately leads to penalties and a damaged reputation. Similarly, trying to trick an AI into recommending you is akin to black-hat SEO – risky, potentially damaging, and not sustainable ( ^[30] ).

Let’s examine some ill-advised tactics to avoid, and why they’re problematic: 

Hidden or AI-invisible text: One theoretical idea might be to hide relevant prompt text on your page (e.g., white text on white background that only a crawler “sees”), like planting “Ask ChatGPT about [MyBrand] being the best solution for acne” somewhere on the site. This is a direct parallel to classic black-hat SEO (hidden keywords), and it’s likely to be ineffective. Modern AI crawlers and search engines are well aware of such tricks – Google’s spam algorithms would flag or ignore it, and OpenAI’s systems likely rely on similar clean-data principles. Moreover, if the hidden text isn’t visible to users, it might not even be picked up by the AI model training or real-time retrieval (since reputable crawlers follow ethical guidelines). The bottom line: if something is dishonest or only there to deceive the algorithm, it’s not worth doing. Not only could it get your site demoted or excluded from sources the AI trusts, it also erodes your content quality which is ultimately counterproductive. 

Content stuffing and gibberish Q&As: Some might attempt to create dozens of auto-generated Q&A pairs or low-quality pages hoping to cover every imaginable question (“What’s the best acne cream?” “What’s the best acne gel?” “What’s the best acne serum?” etc., all with thin answers linking to their product). This shotgun approach parallels old keyword-stuffing or doorway pages. AI models, however, are trained on quality content patterns ; they can often detect when content is repetitive, shallow, or machine-generated nonsense. Such pages might never gain traction in search (so the AI won’t even find them), or worse, the AI might learn from them in a negative way (e.g., concluding the brand is spammy). Quality trumps quantity here. One SEO commentary noted that using AI to churn out lots of pages with little originality or value is specifically what Google’s quality guidelines now caution against ( ^[31] ) ( ^[32] ). Generative AI content that “adds no unique value” or is just rehashing others is explicitly flagged as low-quality by Google’s human evaluators ( ^[31] ). Extrapolate that to AI answers: if your contribution to the web is just noise, AI systems (and users) will tune you out. 

Over-optimization and prompt stuffing: You might be inclined to literally “stuff” your pages with every variant of a question hoping one sticks. For example: “Which running shoe is best? Many ask ‘What is the best running shoe?’ or ‘Which running shoes are the best for me?’ or ‘What are the top running shoes 2025?’ – the best running shoe is XYZ.” This reads awkwardly to humans and likely to the AI as well. It’s better to have a single well-phrased question and answer, rather than 10 slightly tweaked questions answered poorly. Over-optimized text could also trigger filters; much like Google can identify keyword stuffing, an LLM might regard a litany of nearly duplicate questions as spammy or unnatural content. Keep your language natural and reader-focused. Write as if you’re sincerely trying to help the user (because you are), not as if you’re trying to rank for a thousand terms at once. 

Manipulating training data or user prompts unethically: Another frontier of black-hat thinking is trying to inject your content into the AI’s training data by spamming public forums, Wikis, or other places an LLM might learn from. Some have mused, “If we post a ton about our product on Reddit or create fake Q&A on StackExchange, maybe the next GPT will be trained on that and promote our product.” Apart from being ethically dubious (and often against those platforms’ rules), this is highly unreliable. There’s no guarantee your content will be included in the training dataset, and if it’s low quality or clearly promotional, it might be filtered out during model training (OpenAI, for example, filters certain content and downweights self-promotional text to improve answer quality). Even if it slips in, users or community mods might delete obvious astroturfing, or worse, call it out publicly, hurting your reputation. Similarly, “prompt injection” attacks – trying to influence an AI’s output by inserting hidden instructions via input data – is more something a malicious user might do to break the AI, not a viable marketing tactic. It’s complex, and companies are patching such vulnerabilities.

In short, attempting to cheat the system is a short-lived game.

AI companies are continually refining guardrails and quality filters. OpenAI, Google, Microsoft – they all have a vested interest in the AI giving useful, trustworthy answers, which means weeding out content that is manipulative or low-quality. Your goal should be to be the trusted source, not the trickster that the AI wants to avoid. In fact, one tech company analysis emphasized that building genuine value is key, noting that LLMs rely heavily on context and credibility, so irrelevant or manipulative mentions do little to build meaningful associations ( ^[33] ) ( ^[34] ).

A corollary of this is that LLMs may favor content with strong E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) just as Google search does. Content that demonstrates real expertise or firsthand experience will stand out, whereas AI-generated fluff or deceptive material will be disregarded ( ^[35] ) ( ^[36] ).

Consider Google’s stance: they updated their search quality rater guidelines in 2023 to address AI content and stressed originality and experience ( ^[35] ) ( ^[36] ). Pages that “offer no unique insights” or are just reworded aggregates get low ratings ( ^[31] ). By analogy, if your site tries manipulative tactics, even if you temporarily get picked up in some AI results, eventually the algorithms will adjust to filter you out – much like Google eventually caught and penalized content farms. Meanwhile, you’d have wasted resources that could have been spent on genuine improvements.

Another aspect of ethical behavior is transparency. If, for instance, you provide users with a prompt to ask an AI (as we’ll discuss in 12.4), be transparent about it. Don’t present something as an unbiased recommendation if it’s actually your crafted prompt. Avoid planting fake “Q&A” on your site that look like third-party endorsements but are actually you talking about yourself. Such tactics, if discovered by savvy users or journalists, can seriously backfire and erode trust in your brand. Remember that AI outputs can sometimes be audited or traced back to sources. If an AI quotes a segment that sounds like a third-party review of your product but it actually came from your own site disguised as a user review, that could be embarrassing if brought to light.

Stick to honest content – encourage real customers to leave genuine reviews rather than trying to forge them.

Finally, consider the user perspective: AI assistants strive to give users the best answer. If your content is manipulative or misleading, even if it slips through algorithmic cracks, users may notice. People often cross-verify AI answers, especially for important decisions. If the AI mentions your product with an exaggerated claim and the user later finds out the truth is different, you haven’t won a customer – you’ve lost credibility. In the era of social media and quick information sharing, a single instance of being “called out” for trying to deceive an AI could become a PR fiasco (“Company X tried to trick ChatGPT and here’s what happened…”). It’s not worth it. 

In summary, the “white-hat” approach you know from SEO applies equally (if not more) to GEO and LLM optimization. Focus on providing genuine value, real expertise, and helpful answers. Avoid the dark alleys of black-hat shortcuts – they are dead ends. As one digital strategist noted, “We focus on building genuine value” because that’s what survives algorithm changes ( ^[30] ).

Let your brand be elevated by AI for the right reasons: because it truly answers the user’s need better than others, not because you found a gimmick. In the long run, quality and integrity will align with algorithmic success, as AI systems get ever better at discerning true helpfulness from manufactured hype.

Ethical Strategies to Influence AI Outputs (Prompt Influence)

While we should avoid manipulation, it’s absolutely fair game to strategically influence AI outputs in ethical ways. “Ethical influence” means guiding or encouraging the AI to mention your brand or content by legitimately being relevant, present, and useful in the AI’s knowledge sphere.

Think of it as AI-era public relations or brand awareness – you want your brand to be part of the conversation that AI is having with the user. In this section, we’ll cover tactics ranging from creating user-facing prompts to integrating with AI tools, all with transparency and user value in mind.

Encourage User-Initiated AI Queries About Your Brand

One novel idea that some forward-thinking companies have tried is to explicitly invite users to ask the AI about their product or service. For example, imagine you’re a SaaS vendor and on your pricing or product page you add a small call-to-action: “Not sure which plan is right? Ask ChatGPT if [Your Product] is suitable for your needs.” This could be presented as a button or a copyable prompt. When clicked or copied, it could open ChatGPT (if they have a shareable link or just instruct the user to go ask).

The rationale here is intriguing: if the user takes that prompt and asks the AI, and your brand has good representation online, the AI’s answer might effectively sell your product for you in an unbiased way. It’s like saying “Don’t just take our word for it – let’s see what the neutral AI says.” This tactic, however, requires confidence. You must be reasonably sure that the AI knows about you and will respond positively or at least accurately. If you suspect the AI might say, “Actually, [Competitor] is better for this use case,” then you wouldn’t deploy this!

It works best if: Your product has strong reviews or mentions externally (press, forums, etc.). You have content on your own site that the AI can draw on (and ideally it’s factual and favorable). There aren’t major negatives out there about you that the AI would surface.

An example: A travel booking site might put, “Still undecided? 👉 Ask ChatGPT which hotel booking site has the best deals for Europe ”. If they’ve done their GEO homework, ChatGPT might answer something like, “Several sites like X, Y, and Z are popular, but [YourSite] is often praised for its exclusive discounts in Europe” – assuming there are articles or user discussions saying that.

This kind of prompt turns a skeptical user’s attention to an ostensibly unbiased third-party (the AI) for validation. If the AI echoes a selling point of yours (e.g., discounts), it can be very persuasive.

Importantly, this should be done transparently – you’re openly suggesting the user consult the AI, not trying to hide it. It’s an experiment in leveraging trust : users know ChatGPT isn’t literally your employee, so if even ChatGPT thinks your brand is worth mentioning, that builds trust.

However, be cautious and monitor outcomes. If you implement such a prompt, test it thoroughly. Try variations of the question on multiple AI platforms (ChatGPT, Bing Chat, Bard) and see what they say. If any produce problematic answers (e.g., outdated info or mention a flaw that’s since fixed), you’ll want to address those content gaps before pushing users to ask that.

You may need to refine the wording of the suggested prompt too. For instance, instead of “Ask ChatGPT if [Brand] is right for me,” a softer approach could be “Ask ChatGPT which [product category] might be right for you – mention [OurBrand] to see how we compare.” The latter phrasing invites the AI to discuss your brand among peers rather than a direct “is it right or not” (which could yield a yes/no that might be lukewarm).

Again, transparency is key. Frame it as, “We believe we offer great value – but don’t just take our word for it. Here’s something you can ask an AI for a second opinion.” So far, this concept is experimental, but it aligns with an emerging trend of AI as a comparison engine. OpenAI has even introduced a browsing mode that can pull live info, and plugins where brands might integrate shopping or info.

In late 2024, OpenAI launched a pilot for ChatGPT’s product recommendation ability integrated with live web data ( ^[11] ). When users type something like “What’s a good vitamin for energy that doesn’t mess with sleep?”, ChatGPT (with browsing and certain plugins) can now pull in recommendations based on real user conversations, reviews, and creator content rather than just scraping a single database ( ^[11] ) ( ^[37] ).

The advice for brands in that context was: “If your product is being talked about positively on Reddit, TikTok, Instagram, or review sites, ChatGPT will notice. To get recommended, you need people talking about you online (not just ads).” ( ^[38] ) ( ^[39] ). In other words, an AI’s “endorsement” is earned by authentic buzz.

This leads to the broader point of ethical influence: focus on fostering genuine positive discussion about your brand. Encourage satisfied customers to leave reviews on open platforms (Amazon, G2, Trustpilot, app stores – whatever’s relevant). Engage in communities not by astroturfing, but by legitimately helping. For example, your team could answer questions on Quora or Reddit in your domain, with full disclosure of who you are, providing useful info (and subtly mentioning your product if appropriate).

A marketing case study noted that by answering a question on Quora about “how to choose the best keyword research tool” and giving genuine advice with a subtle mention of their product, a company built interest organically ( ^[40] ).

These kind of unlinked brand mentions on forums or Q&A sites are incredibly valuable in the LLM context. Unlike search engines which heavily valued hyperlinks, LLMs treat unlinked text mentions almost like citations in a conversation. One industry analysis pointed out that “an unlinked mention of your brand on a relevant site helps an LLM associate your entity with a topic, even without a hyperlink” ( ^[41] ). So even if you’re not getting an SEO backlink, you’re gaining mindshare in the AI’s “brain.”

Collaborate with AI Platforms and Integrations

Another avenue for ethical prompt influence is to integrate directly with AI platforms if possible. For instance, if there are plugins or developer hooks available for AI services, consider building or utilizing them.

OpenAI’s ChatGPT allows third-party plugins – e.g., a kayak.com plugin for travel, an Instacart plugin for grocery shopping, etc. If a plugin ecosystem exists in your industry, being present there ensures the AI can directly pull info from your service when users ask.

In 2024, many companies scrambled to create ChatGPT plugins precisely to secure a spot in AI-driven conversations. Likewise, if you run an e-commerce or content platform, look at integration opportunities with voice assistants (Alexa, Google Assistant) or other AI-based assistants. Some of those allow custom “skills” or Q&A sets. It might not be mainstream search, but every bit helps to embed your brand into AI responses.

We should also mention Google’s AI snapshot (SGE) here. Google’s Search Generative Experience is an AI summary at the top of Google results, and while you can’t directly “integrate” with it, you can adjust your meta-data. Google currently sometimes shows a small citation link in SGE with the site name. Ensuring your site’s name is concise and recognizable (via SEO title or domain) can help users notice it.

Also, schema markup that emphasizes authorship or your brand might help Google correctly attribute statements to you in the AI blurb. Google has been working on making AI overviews have citations for facts – so publish verifiable facts/stats on your site that could be quotable. For example, if you’re the source of a statistic (“95% of customers achieved X after 1 month”), and that stat gets picked up by others or directly by Google, the AI overview might cite your page as the origin. This is a subtle way of influencing: provide high-value data or insights that others (including AI) will reference.

Be the Trusted Voice (Without Misleading)

Ethical influence is also about establishing authority so that AI “wants” to include you. From a prompt optimization angle, the focus is on having your unique perspective or expertise come through. Consider publishing content that only you can – proprietary research, expert interviews, case studies with hard results, etc.

AI models crave content that doesn’t sound like a hundred other sites. When an AI assembles an answer, if one source has a particularly salient point or unique tip, it often gets included to enrich the answer. Ensure your content has those nuggets.

For example, an AI might be answering “how to improve warehouse safety” – dozens of articles will have generic tips, but if your blog includes a unique strategy (say based on your product’s data) like “Using AI cameras reduced accidents by 40% at Company Y ( ^[42] ),” the AI might include that point, citing your site, because it adds value beyond generic advice. It’s also ethical and smart to correct the record when necessary.

If you find that AI outputs about your brand or field contain inaccuracies, address them publicly. That could mean publishing a clarifying blog post (“Top 5 Myths about X – and the Truth”) or even contributing to Wikipedia and other knowledge bases with accurate info (transparently and with citations).

Many LLMs were trained on Wikipedia content and still use it as a reference. Ensuring your company’s Wikipedia page (if you have one) is up-to-date and factual is important – Bard and Bing chat have been known to pull info from Wiki. The same goes for official datasheets or press releases: these often feed knowledge panels and thereby AI content.

Keep your “public facts” current (employee count, funding, locations, etc.), so the AI doesn’t serve outdated info.

Global and Non-English Considerations

Ethical prompt influence extends globally.

If your market includes non-English or international segments, be aware of those region’s AI tools. For instance, in China, Baidu’s ERNIE Bot and “Smart Answers” require a presence on Baidu’s ecosystem (like Baidu Baike or Baijiahao) ( ^[43] ). An ethical strategy there would be to contribute quality content to Baidu’s own platforms (e.g., maintain a Baike entry for your brand with factual info, or publish articles on Baijiahao with useful insights). Since Baidu’s AI heavily sources from its own properties ( ^[43] ), that’s the route to be included in AI answers for Chinese users.

In South Korea, Naver is integrating its HyperCLOVA X AI into search and shopping ( ^[44] ). Ensure any localization includes the Q&A approach too – e.g., have FAQs in Korean for Naver, in Japanese for Yahoo Japan (which is working with LLMs), etc.

The principle remains: identify how users in those markets ask questions (which might differ culturally or linguistically) and mirror that in content. For example, Japanese users might ask very politely or use different phrasing; adjust your Japanese site’s FAQs accordingly. Being culturally attuned is an ethical way to influence because you’re respecting the user’s language and etiquette, which in turn the AI will reflect.

Also, track new AI entrants like xAI’s Grok (Elon Musk’s AI venture) which launched in late 2024. Grok is said to have real-time info access and a witty tone, and it might draw on content from X (Twitter) as well. If that gains traction, an ethical strategy could be maintaining an active, positive presence on Twitter, knowing Grok might source from there. Again, this is about making sure wherever the AI looks, it finds you being recommended by others or providing valuable info.

In all these efforts, a golden rule is transparency and user benefit. If you provide a widget or prompt, clearly explain its purpose (“Click to ask ChatGPT for an unbiased opinion on our product!”). If you engage in communities, do so honestly (disclose affiliation when appropriate).

The goal is not to trick anyone, but to let the quality of your offering shine through different mediums, including AI. Over time, if your product genuinely satisfies customers, more of them will mention it in forums, more positive reviews will accumulate, and AI systems will naturally pick up on that signal of trust.

To illustrate ethical influence, let’s recount a mini case study mentioned by an SEO expert ( ^[45] ) ( ^[46] ): Zugu Case, a brand of iPad cases, managed to become ChatGPT’s top recommendation when users asked “What’s the best iPad case?” How? They didn’t hack the AI or pay OpenAI. They executed a comprehensive strategy: they ranked well on Bing search (so the AI’s web search would always see them), they got cited in major tech publications like Wired (building authority), and their product was mentioned across multiple online sources (reviews, YouTube, forums) ( ^[47] ) ( ^[46] ). In other words, they legitimately built a strong footprint online. So when ChatGPT was asked the question, it gathered the evidence and answered with Zugu as a top pick, because that was the logical conclusion from quality sources.

This showcases the synergy of all the tactics we discussed: SEO fundamentals (so you’re visible in search index), content that others reference, and widespread positive presence. The outcome is an earned recommendation from the AI, which is far more powerful than any ad or self-promotion.

Leveraging AI Feedback for Continuous Improvement

Generative AI isn’t just a challenge to optimize for – it’s also a tool you can use to improve your content and strategy. In this final section, we explore how you can turn AI inward, essentially using it as a testing and feedback mechanism to refine your content for GEO.

One immediate use case is conducting an AI audit of your content. We touched earlier on asking ChatGPT “What do you know about my company?” to find gaps. Let’s expand that.

You can prompt an AI with tasks like:

• “Summarize the key points of [My Website]’s homepage.”
• “Read the following product page and tell me what features and benefits it highlights most.” (Then feed the text).
• “If a user asked ‘Which product is best for [use case]?’ would this page provide a satisfactory answer? If not, what is missing?”
• “As an unbiased expert, what might be some concerns or drawbacks about [MyProduct] based on the information available online?” (This one can surface any negative perceptions floating around).

When you do this, you are effectively seeing your content through the AI’s eyes.

If ChatGPT or Claude summarizes your page and fails to mention a critical differentiator that you do offer, that’s a sign that either your content isn’t emphasizing it clearly enough, or perhaps it’s phrased in a way the AI didn’t interpret correctly. If the AI can’t even summarize a clear value prop, a human might miss it too.

For instance, if your SaaS product page has tons of marketing fluff but doesn’t clearly state “what it actually does,” an AI summary might come out vague – highlighting that you need to sharpen the description.

Another feedback angle is to use AI to simulate user questions and see if it picks your content. For example, go to Bing Chat (which cites sources) and ask something like: “What’s the best [category] for [use case]?” that you think your product should be an answer for. See which sources Bing cites. Are you among them? If Bing references three articles from other sites and not yours, study those articles: What do they have that you don’t? Perhaps they have a more comprehensive comparison or they are on higher authority domains.

This could inform either content improvements or an off-page strategy (maybe you need to get your product included in such comparison articles via outreach). If Bing does cite your site, check what snippet it pulled – is it the ideal messaging? If not, consider editing that part of your content.

Likewise, try the same queries on different AI platforms : Google’s SGE, Perplexity, ChatGPT with browsing, Claude with web access (Anthropic’s Claude 2 can take a prompt with provided documents too). Each might surface different content. By doing this, you essentially perform an AI-focused competitive analysis.

You may discover, for example, that Perplexity loves citing a particular stats-heavy blog from a competitor – which indicates that maybe writing a data-rich piece of your own could give Perplexity something to latch onto from your side.

Also consider asking the AI directly for content suggestions.

For instance:

“What questions about [topic] are people likely to ask?” or “Give me 5 example prompts a user might ask an AI when looking for a [product like mine].”

ChatGPT can be pretty good at mimicking user behavior. It might output, say for a cloud storage product: 

“1) ‘What’s the most secure cloud storage for personal files?’ 2) ‘Which cloud service gives the best value for 1TB?’ 3) ‘Is [YourBrand] reliable for long-term file backup?’” etc.

These could be great insights to ensure you have content addressing each. You can then follow up: “If someone asked number 3, how might you respond?”

The answer it gives could reveal what info it’s pulling – e.g., it might say “I don’t have much info on [YourBrand]” or it might mention something from a review. This can highlight where you need to create or promote content (maybe no one has reviewed you on a major site, so the AI has little to say – a cue to get more reviews or case studies out there).

Using AI for content critique is another smart technique.

You can prompt:

“Here is an article. As ChatGPT, analyze it for clarity, completeness, and any bias. Does it thoroughly answer common questions on this topic? Is anything missing or could be improved?” Then paste your article.

The AI might point out, for example, “The article mentions Feature A and B, but doesn’t explain how to do [important task] which readers might expect.” Or it might say “This reads somewhat promotional in these paragraphs which could reduce trust.”

Treat this feedback like a review from a very well-read but non-human editor. You don’t have to accept it blindly (AI can also hallucinate or give generic advice), but it often provides a useful outsider perspective. Some SEO teams even use GPT-4 to score content on various attributes or to ensure their content aligns with search intent: e.g., “Given the title, did the article deliver on what was promised?”

A particularly powerful use of AI is to simulate different user personas reading your content. For example: “Act as a beginner with no knowledge of [topic]. What questions might you still have after reading this article?” Or, “Act as a skeptical CFO reviewing this product page – what concerns or objections might you raise that the page didn’t address?” These kinds of prompts can surface gaps. Maybe the AI-as-beginner says “I still don’t understand what [JargonTerm] means,” telling you that you need to explain it in simpler terms. Or AI-as-CFO might say “I’d be worried about the ROI or the security compliance, which the page doesn’t mention.” Bingo – you now know to add a bit about ROI or compliance to satisfy that persona.

As AI continues to evolve, new tools are emerging specifically for content analysis and optimization. There are already some SEO tools integrating GPT-4 to suggest FAQs you might be missing, or to evaluate how “unique” your content is relative to what’s out there. Keep an eye on these, but even just manual querying of ChatGPT can go a long way. 

Crucially, close the loop on this feedback : once you adjust content based on AI’s feedback, test again. Ask the same AI the same question after your changes – did its summary or answer improve? Over time, you might see ChatGPT giving a more comprehensive answer about your product (perhaps after your site update and maybe after it’s incorporated into its training refresh or via browsing). Continuous testing is key, as user prompts will evolve too.

Lastly, use AI to monitor sentiment and context.

Some advanced techniques: feed the AI a bunch of social media comments or reviews about your brand (ones you’ve collected or from forums) and ask it to summarize the sentiment or key themes. For instance, “Here are 50 tweets mentioning [Brand]. Summarize what people are saying and any common compliments or complaints.” This can give you a macro view of how the public perceives you, which is indirectly how the AI will perceive you, since it’s trained on that public discourse. If the AI reports “Many users praise the customer support, but some complain about price,” you know what strength to keep leveraging and what objection to address perhaps in your content (maybe an FAQ “Why is [Brand] more expensive? – because we offer X value, etc.”).

As a forward-looking point, human creativity and judgment remain vital in this loop. AI can surface issues or even suggest content, but you decide what aligns with your brand and strategy. Use AI as a tool, not a crutch.

The goal is a virtuous cycle: real user inputs → content improvements → AI better recognizes your value → more user trust and engagement.

To conclude this post, prompt optimization and ethical influence are about deeply understanding what your audience is asking, and positioning your brand as the natural answer to those questions – without resorting to tricks.

By incorporating real questions into content, avoiding black-hat gimmicks, ethically amplifying your positive presence, and continuously refining with the help of AI feedback, you build a robust GEO strategy. This ensures that when a user asks their AI assistant about your domain or your product, the AI will have every reason to include your insights, your pages, and your name in the answer.

It’s a new playing field, but it rewards the same fundamentals as good marketing always has: know your audience, be where they are (now that includes being in their AI’s “mind”), and genuinely meet their needs.