Revive old content to win AI search

If the content arm of your brand has been active for a few years, I think you have a lot of important things that can be updated to help you stand out more prominently in AI search results — we’ll call this AEO throughout the article.

I get bombarded with questions from product marketers about how to get AEO traction these days. “Review your old content” is a favorite response that often produces an “aha” moment in the other group, perhaps because the nature of AEO is so forward-looking.

That answer raises a few important questions that I will address below.

How to reformat content for better AEO performance?

I like to lean on three principles when dealing with reformatting content. Preparation:

• Topic width and depth.
• Chunk level retrieval.
• Assembling the answer.

Here’s what that means in practice.

Adjust the title width and depth

Build your site using the hub and spoke model. For each main category or keyword theme, create a comprehensive hub page that introduces the broad topic and links to related pages that go deeper into specific sections.

Each spoken page should focus on one visual angle and develop it well enough to capture the unique purpose and intent of the question. Because user questions branch out in different ways, covering multiple angles helps broaden your overall reach for a topic.

Link related pages to each other where it makes sense, and return consistently to the hub as a central reference point. This reinforces how your content connects and gives AI systems clear signals about relationships between topics.

Don’t rely on using the entire page to get the gist. Each episode should be understood independently.

Keep the corridors tight and private. Use one idea per section and keep each paragraph focused on one idea – like our Family Wizard does here:

Prepare a response summary

Summarize complex concepts clearly, then expand with a well-organized “summary” or “Key Takeaway”. Begin answers with a direct, short sentence. favor a clear, authentic, non-advertising tone.

This formatting, from Baseten, puts an easily digestible TL;DR at the top of the post that explains the AI’s suggestions:

How will people react to that formatting?

Start with the premise that AI learning is about clarity, not gimmicks, and this approach appeals to many people who want to quickly understand the content they consume.

AI systems like content where:

• Answers are named, not directed.
• Sections have a clear purpose.
• Important points are easy to raise without rewriting.

That often means being more specific than traditional SEO has ever required – defining terms precisely, summarizing paragraphs and drawing conclusions early. It’s kind of the opposite of keyword-stuffed content that’s overwritten to achieve the perceived “preference” that Google’s algorithm might have for content length.

The only real doubt I have is that AI-generated content might make it too easy. Not every page should be optimized to get a single atomic response, and content that is strategic or opinionated still benefits from news flow.

I try to balance it with:

• It explains first, and then explains more.
• Label the information, then prove it.
• Making the answer obvious before adding complexity.

If done right, this appeals to both AI and humans.

Now, all that being said, LLM-generated content – ​​check your LinkedIn feed if you need examples – very quickly became apparent for what it really is: AI-generated content easily consumed by AI models.

The result can be very transparent, depending on the student, even if your content, as it should always strive to do, includes original POVs, research, and/or data that LLMs cannot find in existing content.

Keep an eye out for what the AI ​​tells you, the dreaded em dash, curved straight line breaks, bullet points with emojis, sentence structures like “It’s not just [X]. That’s right too [Y].” or “It’s more than that [A]. That’s right [B].” and remove them wherever you see them.

How do you prioritize what content to review?

In AEO, prioritizing is less about traffic, which is where most SEO vendors set up KPI intelligence, and more about response rate.

I start by pointing out the content:

• Contains specific technical or proprietary knowledge, preferred by LLMs.
• He answers questions that people ask over and over again, but does not clearly state the answer.
• It is already referred to internally by sales, support, or customers as a “descriptive” key.

Another thing to note: Does the content focus on one of our main products or services, even indirectly? That is basic. Visibility for visibility’s sake isn’t expensive, so make sure it’s naturally aligned with pipeline or revenue growth.

In terms of the types of content that should be prioritized, evergreen reports, tools and guides tend to rise to the top because they already contain structured thinking, if not structured responses. AI systems do not reward originality embedded in prose. They reward clear conclusions, definitions and frameworks.

Here is my first simple AEO test:

• Can an AI model confidently quote or summarize this page as it is?
• Will it know what question this page answers within the first few seconds?
• Are the keys taken labeled or displayed?

How do you look at metadata when reviewing AEO content?

Before I dive into the how, I will mention that these elements have a different function for AEO than for SEO. In SEO, they work like ranking levels. In AEO, they mainly serve as context anchors.

Let’s break down each key component of metadata and show how those differences should play out.

Title tags

Title tags work like page titles for traditional SEO. In AEO, make them more descriptive about the main response of the page or function.

So, for SEO purposes, a title tag that reads “Time replay software” can be rewritten in AEO as “Session replay: what it is, when to use it and when not to use it.” Title tags with more context give AI systems clear signals about how and when to cite content.

Headings (H1-H3)

In traditional SEO, title tags are used to identify categories, for example, “compliance monitoring.”

In AEO, I use them to map to specific questions or claims. Possible updated versions of the above would be:

• What is compliance monitoring?
• Why is compliance monitoring important for companies in the {x} vertical?
• Common problems caused by lack of compliance monitoring
• When should a CTO invest in compliance monitoring?

To stress-test the header tags, try answering them. If it takes you more than a few sentences to answer your question or prove your assertion clearly and convincingly, it’s probably the wrong question and no user will type it into ChatGPT.

Meta descriptions

Meta descriptions are pieces of extended text that may or may not be pulled into SERPs by traditional SEO, but serve to describe content. In AEO, they act as a suppressed intent signal. AI systems, like SERPs, may choose not to cite the meta description, but good ones help reinforce:

• Who owns the content.
• What problem does it solve.
• How it should be framed.

Through an AEO lens, I view meta descriptions as a one-sentence summary note for both users and LLMs.

What changes – and what doesn’t – in the transition to AEO

You may have noticed a theme here – while, in general, what’s good for SEO is what’s good for AEO, there are material differences in the two fields. Knowing what they are and how to adapt accordingly can pay off in AI search visibility.

I’m not arguing that your content strategy or themes should rotate. But knowing that AI models learn and consume content differently than traditional SEO algorithms is important and should be factored into how you recover your evergreen work from months and years ago.