Answer Engine Optimization - What Does This Mean for Brands?

Answer Engine Optimization is the process of organizing content so that large language models (LLMs) such as ChatGPT can interpret, reference, and recommend your brand when answering user queries. For your brand to be recognized by an LLM, you must understand how these models learn from data. LLMs are trained to complete sentences. Take the example: “Life is like a box of chocolate.” During training, the model would mask a random word and attempt to predict it. In order for your content to appear in an LLM’s response, it needs to become part of its training dataset.

Here are some tips for businesses:

Q&A Format: Train The Model With Conversations

Simply publishing your product catalog online won’t ensure that LLMs use it. They may scrape it, but they won't prioritize it. Standard marketing copy isn’t enough. LLMs learn through natural dialogue — not slogans or taglines.

Brands need to transition from static, keyword-driven content to dynamic, conversational material. Think less like a brochure and more like a knowledgeable sales rep responding to real customer inquiries. This is where traditional SEO falls short — it was built around isolated keywords. LLMs require context.

Helpful And Authentic: Surprise The Model

LLMs ignore information they already possess. If your content states “The earth is round,” it likely won’t register since the model already contains that knowledge.

You should focus on uncovering something new or lesser-known about your brand, product, or industry. The most impactful content is fresh, useful, and rooted in genuine interaction.

Authoritative: Be A Source Worth Citing

Some principles remain unchanged. Just like with SEO, credibility still plays a crucial role. Content that earns links, citations, and validation across multiple sources builds authority. Low-quality or spammy content doesn’t work. If your brand lacks trust or a distinct voice, LLMs won’t echo it.

The Monitoring Mirage Of Answer Engine Optimization

Whenever a new technology trend emerges — AEO being no exception — tech companies rush to develop monitoring tools. The latest tools, such as Profound, Daydream, and Goodie, offer dashboards to track brand mentions across AI platforms like ChatGPT and Perplexity.

However, there's a challenge: LLMs behave differently than search engines. They retain memory. Unlike Google, which historically didn’t remember past searches, LLMs build context over time. Ask ChatGPT what it remembers about you — you’ll see. These models evolve based on previous interactions, which influences future outputs. That makes it impossible for any dashboard to accurately monitor all possible responses, as each user’s experience is shaped by their unique conversation history.

Best Measurement Of LLM Traffic

So what’s a better way to measure impact? Track actual traffic. Monitor what’s coming into your site from platforms like ChatGPT, Gemini, or Perplexity. It’s cost-effective, accurate, and shows real results.

How To Create LLM-Friendly Content

Monitoring alone only tells half the story. To influence LLM training data, you need to create new, valuable brand content. The old SEO strategies no longer apply. Your brand has unique insights and a vision. Don’t bury them behind generic product listings.

Take the example of someone searching for a “retirement watch.” Instead of listing five products, explain what defines a great retirement watch. Is it legacy? Readability? Emotional significance? Engage users in a meaningful conversation. That kind of context is exactly what LLMs are designed to absorb.

In summary: showcase the conversations you're already having. Review your site search logs, sales scripts, and support chats. That’s valuable data. LLMs respond best to content that sounds like a helpful human.

Here’s how to proceed:

Understand User Intent & Frame Questions

• Build content around real questions people ask.
• Offer concise, direct answers at the beginning.

Focus On Meaningful Content

• Don’t just describe features. Explain relevance.
• Share stories — they’re more memorable.

Optimize For Voice & Conversation

• Use phrasing that feels natural and aligns with your brand tone.
• Prioritize “who,” “what,” “where,” “when,” “why,” and “how” formats.

Some brands already have this kind of content available publicly — in forums, Reddit discussions, or customer reviews. That content naturally surfaces in LLM outputs.

Other brands have rich insights hidden within internal support logs or CRM systems. Extract that data. Structure it. Publish it. Make it accessible. Tools like Google’s Vertex, Meta’s Llama, or specialized solutions like r2decide can help streamline this process.

What’s Next After Answer Engine Optimization?

AEO is just the start. Two major changes are on the horizon — both will significantly affect how brands appear in the AI era.

Paid Discovery Will Change

LLMs are set to incorporate advertising directly into their responses. Google, Perplexity, and OpenAI have all confirmed plans for this. Expect this shift to occur by early 2025 — possibly sooner.

But don’t expect traditional ads. These models will provide recommendations as part of the conversation. Since users pay for services like ChatGPT, the economic model shifts. New ad delivery platforms will emerge, feeding LLMs with tailored conversational ad snippets matched to each user’s query.

The emphasis won’t be on pushing sales, but on offering assistance. This means brands will need their own brand-side LLM — a system capable of representing the company within these dialogues and suggesting the right product at the right time.

Agents Everywhere Via Model Context Protocols

The next big wave involves agents that handle full transactions within the LLM interface.

OpenAI has introduced Model Context Protocols (MCPs), enabling ChatGPT to do much more than chat. It can check inventory, answer personalized questions, and even schedule deliveries. Sam Altman has described the goal as creating personal AI assistants that can take action — not just provide information.

For brands, this means developing your own agent layer — a system that connects to these conversations, delivers customized responses, and completes the customer journey without redirecting users to your website.

Brands Need Their Own Discovery layer

To stay visible and relevant, brands must establish their own discovery layer: content that speaks the language of LLMs — conversational, helpful, and ready to be recommended.

This isn’t speculation. The transformation is already happening. Want to explore further? Connect with me on LinkedIn.

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