Answer engine optimization strategy beyond basic SEO and AEO tactics
If you’re not in the trenches of search every single day, it’s hard to know how seriously to take answer engine optimization strategy. There are two dominant camps right now: those who see generative AI as the most disruptive shift search has ever experienced, and those who argue that AEO (or GEO) is simply an extension of traditional SEO.
Predictably, the truth lives somewhere in the middle — a lot of AEO is SEO, with some pivots, enhancements, or attention diverted to prominent tactics that help brands gain visibility in AI tools. On the other hand, you can gain visibility in AI tools without ranking well in traditional SEO listings; the tactics can be separated.
What‘s harder to separate is your brand from the consequences of ignoring AI’s impact on search. Google’s AI Overviews (AIO) is taking clicks from websites; clicks drop by 61% when AIO is present and more alarmingly, your potential customers are busy asking AI tools about brands before they decide to create a shortlist. If your brand isn’t getting visibility for those early searches, you’re out of the race before the buyer has even discovered your website.
If you’re creating an answer engine optimization strategy and you want something more nuanced than “just do good SEO,” this is the article for you. I’ll cover how answer engines choose what to cite, where SEO still does the heavy lifting, and what additional work is required to appear in AI-generated answers.
Table of Contents
- AEO strategy foundations: how AI engines and LLMs pick sources.
- Answer engine optimization strategy beyond the basics
- How to format AEO content so LLMs extract and cite it.
- How to build authority so answer engines trust you.
- How to measure success from your AEO strategy.
- Connect visibility to pipeline in your CRM.
- Answer engine optimization mistakes to avoid.
- Frequently Asked Questions About AEO Strategy
AEO strategy foundations: how AI engines and LLMs pick sources.
The models that power LLMs, like ChatGPT, are trained on a combination of:
- Publicly available internet content
- Licensed third-party data
- Information generated by human trainers and users
Together, these sources shape how models understand entities, topics, and relationships across the web.
Read more about the foundation of ChatGPT here.
A common misconception is that LLMs were trained on a bunch of sources and that their answers are now set, but this isn’t the case.
Enter Retrieval Augmented Generation (RAG).
RAG improves AI responses by adding external context when a question is asked. Rather than relying only on what a model learned during training, RAG allows it to pull in relevant information to produce (in theory!) more accurate, grounded answers.
Here’s what a basic RAG workflow looks like:
In this search evolution, your content needs to be retrievable, which means being clear in your …read more
Source:: HubSpot Blog
