What is Answer Engine Optimization? How to Get Found in LMMs

How to structure web content so AI answer engines can find it, trust it, and cite it.

This playbook is a deep technical dive into Answer Engine Optimization (AEO)—the practice of making your content findable, parsable, and citable by LMMs and AI answer engines. Unlike our regular posts, this guide focuses on implementation details, schema markup, crawler behavior, measurement frameworks, and structural techniques. It’s designed for content strategists, SEOs, and technical marketers building for LLM-first environments.

TL;DR:

What is AEO? (And why do people call it that?)

Answer Engine Optimization (AEO) is the process of organizing and enhancing web content so that AI systems such as ChatGPT, Perplexity, Bing, and Google's AI Overviews can locate, analyze, verify, and reference your pages within synthesized answers.

You may come across terms like LLM SEO, Generative Engine Optimization (GEO), and LLM Optimization (LLMO) used interchangeably. This article uses AEO throughout.

AEO vs. Traditional SEO

Traditional SEO Answer Engine Optimization Traditional SEO Answer Engine Optimization
Goal: Blue‑link ranking Goal: Inclusion and citations inside AI answers
Unit: Whole pages Unit: Passages, entities, tables, steps
Query model: Keywords Query model: Questions and tasks
Primary signals: Links and on‑page relevance Primary signals: Clarity, evidence, schema, freshness, author identity
Success metrics: Positions, organic clickSuccess metrics: Share‑of‑answer, citation count and accuracy, assisted conversions

AEO does not replace SEO. It refocuses on answerability and verifiability at the passage level. To the average business owner or organization leadership, this can seem daunting. We have put together an implementation guide to help clarify the best way to implement AEO on your website while preserving the native SEO and content-based traffic.

The 10‑Step AEO Implementation Guide

1) Map answer intents

Begin by creating an inventory of questions and answers for each topic cluster that interests you. Include various categories such as definitions, how-tos, comparisons, pricing, specifications, pros and cons, troubleshooting, and benchmarks. Assign a single primary intent to each page to ensure it has a clear purpose. Additionally, group closely related questions into a small FAQ section at the end of the page.

2) Package answers for machines

Open each page with a direct answer of 40 to 120 words that clearly addresses the main question. Follow this with the method used, supporting details, and examples. Organize content into short sections with clear H2 and H3 headings that reflect how people ask questions. Ensure each paragraph contains only one main idea so that passages can be easily extracted. Incorporate structural elements like numbered steps, checklists, tables, callouts, and glossaries to help parsers extract facts without ambiguity.

3) Strengthen entity signals

Create canonical pages for your key entities, including products, features, concepts, and authors, and ensure consistent naming throughout the site. Link related pages back to these main hubs using descriptive anchor text. Provide detailed information about organizations and individuals with bios and credentials, and utilize BreadcrumbList to clarify the location of each page within your information architecture.

4) Provide evidence

Include numbers, methods, and dates next to the claims they support, and cite primary sources whenever possible. If you own the data, publish it as a downloadable CSV or JSON file, and present it in clean HTML tables. Include figures with captions and a brief methodology section to help answer engines verify the origin and quality of your evidence.

5) Add schema markup

Use JSON-LD to implement the core types that are relevant to each page, including FAQPage, HowTo, Product, Article, Organization, Person, and BreadcrumbList. When appropriate, enhance the structured data with SpecialAnnouncement for time-sensitive notices, Dataset for original research, and VideoObject or ImageObject when rich media can provide additional clarity. Be sure to validate your implementation using testing tools and address any errors before publishing.

Examples

FAQPage

<script type="application/ld+json">

{

  "@context": "https://schema.org",

  "@type": "FAQPage",

  "mainEntity": [{

    "@type": "Question",

    "name": "What is Answer Engine Optimization?",

    "acceptedAnswer": {"@type": "Answer", "text": "AEO is optimizing web content so AI answer engines and LLMs can find, verify, and cite it."}

  }]

}

</script>

Dataset

<script type="application/ld+json">

{

  "@context": "https://schema.org",

  "@type": "Dataset",

  "name": "2025 LLM Answer Coverage Study",

  "description": "Monthly measurements of share‑of‑answer across major AI engines for 150 queries.",

  "creator": {"@type": "Organization", "name": "ExampleCo"},

  "distribution": [{"@type": "DataDownload", "encodingFormat": "text/csv", "contentUrl": "https://example.com/llm-coverage.csv"}],

  "dateModified": "2025-08-01"

}

</script>

SpecialAnnouncement

<script type="application/ld+json">

{

  "@context":"https://schema.org",

  "@type": "SpecialAnnouncement",

  "name": "Pricing update effective Sept 1",

  "text": "Updated pricing for the enterprise tier effective Sept 1.",

  "datePosted": "2025-08-15",

  "expires": "2025-10-01"

}

</script>

VideoObject

<script type="application/ld+json">

{

  "@context":"https://schema.org",

  "@type": "VideoObject",

  "name": "How to structure AEO answer blocks",

  "thumbnailUrl":["https://example.com/thumb.jpg"],

  "uploadDate": "2025-08-10",

  "description": "A 5‑minute guide to writing answer‑first pages.",

  "duration": "PT5M"

}

</script>

6) Ensure technical access

To ensure proper indexing by major AI crawlers, explicitly allow them in your robots.txt file and align this with page-level meta tags when necessary. The typical allowlist of crawlers includes GPTBot, ChatGPT-User, Claude-Web, PerplexityBot, Googlebot, Google-Extended, Bingbot, and Meta-ExternalAgent. It's important to keep the lead answer block and key tables in the initial HTML so that search engines with limited rendering capabilities can still access the essential information. Additionally, publish an XML sitemap that includes all answer pages.

robots.txt

User-agent: GPTBot

Allow: /

User-agent: ChatGPT-User

Allow: /

User-agent: Claude-Web

Allow: /

User-agent: PerplexityBot

Allow: /

User-agent: Google-Extended

Allow: /

User-agent: Googlebot

Allow: /

User-agent: Bingbot

Allow: /

Sitemap: https://example.com/sitemap.xml

Page‑level meta

<meta name="robots" content="index,follow,max-snippet:-1,max-image-preview:large">

<meta name="googlebot" content="index,follow">

7) Optimize passages

Write headings that resemble the questions users ask, then answer them directly. After that, add nuance, examples, and limitations. Keep the lead answer concise, include related synonyms and entity aliases nearby, and organize the surrounding context so that any single paragraph can stand on its own when pulled into an AI-generated response.

8) Build off‑page authority

Use digital PR and useful tools or datasets to acquire citations from reputable sources, such as industry journals, .edu or .gov domains, standards organizations, GitHub, and specialized wikis. When others reference your work, encourage the use of exact anchor text that matches your entity names to ensure clear association signals.

9) Freshness and versioning

Schedule time-sensitive pages for updates. When you revise content, display an updated date. Maintain a public change log for methodology pages, and either annotate or replace outdated figures. Remove any content that cannot be updated accurately to prevent search engines from indexing outdated claims.

10) Measure and improve

Establish a fixed query panel for each cluster and conduct tests across different search engines on a monthly basis. Record where your content appears, how it is cited, any confidence indicators, and whether the provided answer appears to be complete. Monitor metrics such as share of answer, citation count and accuracy, answer confidence, answer completeness, entity association strength, opportunities for zero results, and assisted conversions. After making edits, rerun the panel and document the changes.

Example: Turning a generic listicle into an answerable page

Before: A long article on "best project management tools" with buried comparisons and generic blurbs.

After:

Result: Clear passages that engines can quote, verify, and cite.

Pitfalls to avoid

Final checklist

Content and structure: Ensures pages are built around clear questions and direct, structured answers.

Evidence and media: Confirms claims are sourced, dated, and supported with usable formats and captions.

Schema markup: Verifies structured data types are implemented and validated for key content types.

Technical access and performance: Checks AI crawlers can access content and that it loads quickly and cleanly.

Authority: Validates visible author/org credibility and external recognition.

Measurement and governance: Tracks inclusion, accuracy, and update hygiene at scale.


Conclusion

Search engines prefer content that is clear, verifiable, and well-organized. By optimizing your content for these criteria, you can align it with how these systems process and understand information. Follow the steps outlined earlier, track the appropriate key performance indicators (KPIs), and make adjustments consistently. The result will be lasting visibility in AI-generated answers and enhanced brand authority, which is crucial as users increasingly rely on this information to make decisions.

If this sounds like a lot, well you're not wrong. Implementing AEO effectively takes technical skill, strategic content, and deep platform knowledge. When you need help handling an AEO campaign like this, you want to talk to an expert. Twothirty Media are AEO experts in Pittsburgh and we work with organizations all over the country and world on optimization. If you're looking for expert help optimizing your content for LLMs and answer engines, please reach out!

Has the AI Bubble Popped?

And if It Hasn't, Will It?

AI is everywhere now, and with its rise, more people are starting to spot it—and not always in a good way. It's cheap, it's accessible, and that's attracted a whole bunch of folks who might not have the best intentions. Scammers, spammers, and other unsavory types are jumping on the AI bandwagon, and it's starting to give the whole thing a bad name. So, are we looking at an AI bubble that's ready to pop? Or is this just how it goes when something new and shiny gets overused, and we'll eventually see things level out? Let's dig into whether AI is heading for a crash or just a course correction as people wake up to what it's really doing.

Whether there is a bubble or not, as the world begins to grasp the real-world applications of AI, there will be winners and losers in every industry. This article is, in a way, a follow-up to our article Will AI Replace Artists? The Future of Art and Design in the Age of Artificial Intelligence. In it, we will explore the possibility of AI creating a bubble and that bubble popping. But before we look ahead, first, we have to look at where we have been. Let's review what a bubble pop looks like.

Understanding Tech Bubbles: Past and Present

Dot-com Bubble (Late 1990s - Early 2000s)

Real Estate Bubble (Mid-2000s)

Cryptocurrency Bubble (2017-2018)

3 Signs of a Bursting AI Bubble:

Decreased Funding and Investment

A drop in venture capital funding for AI startups, fewer IPOs, or lower valuations could signal a market correction. You might see headlines like "AI Startup Funding Drops by 50% in Q2" to indicate such a trend. Not something I am seeing yet.

Mass Layoffs in AI Companies

Large-scale layoffs or closures of AI startups can signal financial stress within the industry. News stories such as "Leading AI Firm Announces Layoffs of 30% of Workforce" would highlight these concerns. This also isn't something we are seeing.

Failure of High-Profile AI Projects

Public failures of well-known AI projects can shake confidence in AI solutions. We are seeing some examples of this one. Reports like "AI Gone Wrong: An Updated List of AI Errors, Mistakes and Failures" for example. While the online-chatter-sentiment (hell of a metric, I know) of AI success is currently on a bit of a downtrend, this is still not the case as far as investor interest goes, as far as I can tell.

What Remains After the Bubble Deflates?

When tech bubbles deflate, they can leave behind valuable innovations and infrastructure. Let's look at the nearest relevant example for us marketers.

Dot-com Bubble

The internet infrastructure built during the dot-com era laid the foundation for the modern internet. Companies like Amazon and Google emerged stronger, becoming industry giants.

Similarly, in the post-AI bubble, robust AI infrastructure, including improved machine learning frameworks and data processing capabilities, could continue to drive Innovation in various sectors. One of AI's most tangible benefits, especially in marketing, is how it simplifies mundane tasks. For example, prompt engineering allows project managers to use AI tools to summarize tasks and start a production calendar very efficiently. But does this mean AI will replace project managers and other roles entirely? No way. Much like the chainsaw revolutionized the lumber industry without replacing lumberjacks, AI will enhance the worker's current capabilities without taking over completely.

Public Sentiment and Ethical Concerns

Quality and Authenticity Concerns

There is growing skepticism about AI-generated content, with many believing it needs more depth and nuance than human-created works. Headlines like "How AI-generated content could both fuel disinformation and improve fact-checking" capture this sentiment. AI videos and images being used to impersonate or trick the public is a valid concern that has caused growing trust problems across the internet already, such as Meta trying to flag AI-generated content.

Job Displacement Fears

AI and automation have raised fears about job losses among many marketers. Reports such as "Artists Are Revolting Against AI Art on ArtStation" reflect these anxieties. This has been mentioned a lot in multimedia and marketing - I've read suggestions that everything from designers to developers and video editors will be replaced, and I've been seeing a lot more AI art. However, when it is art that would normally have been handled by an artist, they are typically seen as giving the product a low quality, at least in the most obvious cases. This makes sense – AI art as a primary designer is cutting corners, but anecdotally, savvy consumers are beginning to take note and form opinions about products that build their brands around AI art. Brand managers – take heed.

Ethical and Privacy Concerns

As AI systems collect and use personal data, concerns about privacy violations are increasing, too. AI companies come under intense scrutiny for potential data privacy violations, and there is growing concern about how these companies handle personal data. The Federal Trade Commission (FTC) has emphasized that AI companies must uphold their privacy and confidentiality commitments if the AI models are offered as part of a service. These companies often require large amounts of data to train their models, which raises significant privacy concerns when personal and sensitive data is involved.

Additionally, California's ongoing legislative efforts, such as the amendments to the California Consumer Privacy Act (CCPA), are aiming to strengthen data privacy regulations around AI systems. These amendments include ensuring that personal information processed by AI systems remains subject to strict privacy obligations, highlighting the increasing legal focus on protecting consumer data in the age of AI.

While at the surface level, this might all seem like a negative response at a federal level, the truth is it was unlikely for AI to develop without some amount of regulatory oversight. The addition of regulations into AI indicates lawmakers expect that it will take hold in a longer-term way.

Can Regulatory Frameworks Increase Stability?

Developing regulations can ensure responsible AI development and build long-term trust and stability in the technology. This regulatory oversight can prevent unchecked growth that leads to bubbles.

The Potential for a Soft Deflation

A bubble popping doesn't really have to be a dramatic event, either. There is potential for a soft deflation or gradual adjustment in the usage of AI. A soft deflation in the AI market would involve a steady correction, where investment becomes more targeted and focused on sustainable innovations rather than speculative ventures.

So what is a better model of growth than the bubble that expands so quickly it must burst? Even sustained Innovation allows for integration and solid uses to develop the technology. Continuous, measured advancements in AI technology can maintain investor confidence, even if the pace of hype-driven funding slows. This steady progress ensures that AI remains relevant and valuable.

What if AI is Not a Bubble?

On the other hand, if it turns out that AI isn't a bubble at all, it might represent a significant technological shift, similar to the internet or mobile revolutions. AI's integration into various sectors may indicate a foundational technology undergoing natural growth and adjustment to better suit the industry's needs, not unlike an evolution. This technological evolution could see the role of AI as a tool changing as it integrates into new and surprising ways.

The ongoing integration that we are already seeing doesn't indicate any slowing down. AI is steadily becoming a part of healthcare, finance, and transportation, among other fields. This suggests that AI is not just a fleeting trend but a technology that is here to stay. Companies are increasingly relying on AI for efficiency and Innovation. This reliance and adaptability indicate that AI has a robust and enduring role in modern business practices.

AI in Marketing, Media, and Multimedia

Drawing Conclusions: Has the AI Bubble Popped?

I'd say that, as of this time of writing (late August, 2024), a small AI bubble has been forming that has not popped. Still, the AI industry is experiencing adjustments, and inevitably, there will be more. The market might see a soft deflation, where growth becomes more sustainable and grounded in real-world applications. Alternatively, AI might represent a foundational technology that continues to evolve and integrate deeply into various sectors.

Final Thoughts

AI continues to be a game-changer in marketing, media, and multimedia. Whether we're witnessing a bubble or a transformative technological shift, AI's impact is undeniable and likely to shape the future of many industries with many diverse applications for marketers beyond content. AI is used in chatbots, virtual assistants, and dynamic pricing. Soon, AI could be utilized in marketing to make incremental improvements in algorithms for ad targeting, improved sentiment analysis, and better customer journey mapping. AI's ability to process data, identify patterns, predict consumer behavior, and, yes, develop content makes it invaluable for marketers, but without a human to parse through that data, it is still rarely worthy of a human audience.

Will AI Replace Artists? The Future of Art and Design in the Age of Artificial Intelligence

Recently, I stumbled upon something quite peculiar on a grocery site I often use. The recipe photos, at first glance ordinary, held some bizarre surprises. Lemons with egg yolks, bacon jam merging seamlessly with its jar, pastries exhibiting impossible Euclidean geometries, turkeys with extra legs, mutant strawberries, and bizarre, indecipherable alien scripts. These oddities confirmed my suspicion: the website has embraced AI-generated imagery.

As somebody who has friends, family, and colleagues who work as food photographers (a specialty I have a ton of interest in and respect for), this hurt to see. At the same time, I know that without some something like regulations, the temptation to shift towards cheaper alternatives is usually too great to ignore for most business owners. Traditional food photo shoots are expensive and can be logistically challenging, especially for recipe content that doesn't directly drive revenue, but thankfully for my friends in the biz, the AI results speak for themselves. And they are strange.

However, from what I can tell, it is certain that AI-generated images will improve significantly in time. This raises questions about the future of art in the digital domain. Art, and yes even food photography, has always been a medium for human expression, creativity, and emotion. Before now, we have exclusively celebrated the artist's unique perspective. But can AI replace artists?

Understanding AI in Art

Before we explore the potential of AI replacing artists, first we need to grasp what AI in art really means. Artificial Intelligence, in simple terms, is about computer systems doing things that typically require human brains – like learning, solving problems, and understanding languages. When applied to art, these systems can help generate entire creative works based on human prompts or assist artists with more minute details in their creative process.

AI-generated art involves algorithms that learn from an array of existing artwork to create something new. These algorithms analyze patterns, colors, shapes, and styles from potentially thousands or more pieces before generating their own unique pieces. This process is often referred to as machine learning or deep learning.

AI and the Future of Food Photography

In the context of food photography, in the United States we have regulations that mandate the use of edible items. For example, advertisers need to display actual food when depicting food for sale. No substitutes, everything shown as food must be edible. This has led to strange practices such as using mashed potatoes to stand in for iced cream, as they're more photogenic under hot studio lights. It's a fine line between making food look great and staying within legal boundaries.

Does AI's entry into this space complicate this? Since AI-generated images aren't bound by the same rules — they're creating representations, not photographs — we may see a legal and ethical debate unfold. Is AI food alright on packaging and commercials for products you can purchase? What about for recipes? What will we tolerate as a society? How does AI imagery even apply to our standards of realism and truth in advertising? It's certainly an area ripe for potential regulation and debate.

Photography Beyond the Reach of AI – Limitations and Drawbacks

While AI shows promise in specific photography niches, it's important to remember its limitations. For some audiences and users, there is no replacement for legitimate footage. Documentary photography, for instance, relies on capturing real, unscripted moments, interactions, and events. The news media, one would hope, would adopt some best practices on the ethical use of AI-generated visual content and not misrepresent the footage they present before regulation becomes necessary. The authenticity and storytelling power of a human behind the camera, witnessing and recording life as it unfolds, remains unparalleled by AI. I believe the human demand for authentic imagery will be what saves food photographers from AI replacing them in the long term in the same way it will preserve documentary film crews and news media. There will always be a market for authentic and documentary media.

While I am talking about limitations, I should mention that the reason I can feel fairly safe in displaying the “food” images above is due to perhaps the biggest current drawback with AI art – the courts have determined that an AI created image cannot be copyrighted under U.S. law, stating that creative works must have human authors to be copyrightable.

The Role of Human Creativity in Art and Design

Despite the impressive capabilities of artificial intelligence, it lacks one crucial element that every artist possesses – human emotion. Art is not merely about creating visually appealing pieces; it's also about conveying emotions, telling stories, and expressing individuality. These are aspects that an algorithm cannot replicate.

Human designers have the ability to understand cultural nuances, historical contexts, and personal experiences - all these elements contribute to their creative process. They can empathize with their audience and create designs that resonate on a deeper emotional level.

Furthermore, while AI can mimic styles based on data inputted into its system, it cannot innovate or think outside the box like a human designer can. It doesn't have personal experiences, intuition, or emotions to draw inspiration from, limiting its creative potential. It cannot understand its audience or the client. Humans are still needed for that. In the case of my online shopping service, a human could have certainly prevented the worst of the images from making it onto the website. Clearly, at least for now, the best practice should involve oversight over this content at an absolute minimum.

So Can AI Replace Artists?

While the advancements in AI have led to impressive feats in the worlds of art and media, the question remains: Can AI ever replace artists? To answer this, we need to consider what makes art truly 'art'.

Art involves making conscious decisions about what to create and how to create it. An artist's choices in terms of color, composition, texture, and form are influenced by their thoughts, feelings, experiences, and cultural background. AI doesn't possess these human qualities; it creates based on programmed algorithms. There is a case to be made that it is the thought process itself that makes art, and that making art is impossible without conscious thought.

Reluctant Adoption, But Not Replacement

So, will AI make artists obsolete? The answer is not as straightforward as it may seem. While AI is definitely shaking things up in the art and design world, it's not about to fully replace human designers. But there’s no putting the genie back in the bottle either; AI is already here.

At least when it comes to multimedia marketing, what's more likely than AI taking over your field is a future where more humans in your field use AI. By taking the best of what AI is good at – efficiency and crunching data – human artists will likely continue to do what they have been doing since the beginning – using art to express themselves.

The Future of AI in Art and Design

While it seems unlikely that AI will replace artists entirely in the foreseeable future, it certainly has a role to play in the evolution of art. AI can serve as a tool for artists to explore new creative avenues and push boundaries.

Moreover, the intersection of AI and art opens up exciting possibilities for interactive and immersive art experiences. Artists can create dynamic artworks that respond to viewers' movements or change over time using AI technologies.

In conclusion, while AI in art has made significant strides and offers exciting possibilities for creativity and innovation, it cannot replace the emotional depth, personal expression, and conscious decision-making inherent in human-created art. The unique perspective each artist brings to their work is irreplaceable by any algorithm. However, like it or not, AI will be a powerful tool in the hands of artists, helping them push the boundaries of creativity and explore new artistic horizons. Creativity, for now, remains a uniquely human trait. And while machines may mimic or assist us in creating art or designs, they cannot replace the human touch that makes each piece unique. So instead of asking "Will AI replace artists and designers?", perhaps we should be asking "How can legitimate artists effectively collaborate with AI?" The answer to this question will define the future of art and design.