Weak ties vs strong ties

AEO (Answer Engine Optimization) Explained: How to Win the Future of Search

You ask your phone a question while rushing to a meeting. In seconds, it reads back a clear, direct answer. No clicking, no scrolling, no distractions. For businesses, that answer is either a golden opportunity or a missed one. The way people search has changed, and Answer Engine Optimization (AEO) is what decides who gets seen and who gets left behind.

Whether your customers use Google, Bing or ChatGPT, AEO is the practice of shaping your content so it is chosen by answer engines, the LLMs and AI systems that now sit at the heart of search. These models process language in ways that go far beyond keywords. They evaluate context, understand intent, and surface responses that feel natural to the user. If your content is not optimized for how these systems work, you risk becoming invisible.

In this article, we will unpack what AEO really means, explore how LLMs process and deliver information, and show you the building blocks like embeddings, semantic triples, and entities that determine whether your brand shows up when it matters most. The rules of search have shifted, and the time to adapt is now.

What’s ahead

• What is AEO?
• How to LLMs Work: The Foundation
• Core Concepts
  • Vector Embeddings
  • Semantic Triples
  • Entities
• Weak Ties and Strong Ties
• How to Optimize Subject-Entity Ties and Improve Overall Visibility

What is AEO (Answer Engine Optimization)?

AEO (Answer Engine Optimization) improves visibility in search by optimizing content to answer user queries directly. AEO focuses on structured data, featured snippets, and natural language that aligns with search intent. It helps brands appear in voice search, Google’s Knowledge Graph, and zero-click results.

How Do LLMs Work?

LLMs (Large Language Models) work by predicting the next word in a sequence using patterns learned from massive text datasets. They use transformer architectures to process input tokens, apply attention mechanisms to capture context, and generate coherent responses based on statistical probabilities.

Let’s look at an example because that definition is loaded with technical jargon. 

The job of an LLM when creating output is to predict the next word based on probabilities. Take the word “digital.” There are hundreds (maybe thousands) of words that would be grammatically correct to use as the next word in a sequence. However, some words are more probable.

Looking at the word “digital,” the probabilities might be something like this:

Next word	Probablitiy
marketing	.896
converter	.639
asset	.402
currency	.724

In the example, we’re only looking at one word. In reality, an LLM will look at multiple word strings to get a better lock on the next word. Each additional word provides more context, which in turn limits the number of possibilities for the next word.

Similar to human communication, if there are multiple potential next steps, the LLM won’t choose the same configuration every time. This is called randomness. The amount of randomness can be controlled in a model by setting a hyperparameter called temperature. The nuance of temperature is beyond this article; however, I wanted to point it out because it’s one of the reasons why the same prompt entered by two different people, or even the same person, will get a different response from the LLM.

Machines learn these probabilities by analyzing existing texts and then testing its abilities. One test of an LLM’s ability is the masked word test, where the LLM is fed a sentence with a word missing and is asked to guess the missing word. If the model guesses correctly, it’s given a reward. As in life, the goal is to maximize the reward; thus, the more correct guesses the model makes, the more accurate the system.

Vector Embeddings

Now that we have a general understanding of how an LLM works, we need to cover the question of how a machine understands words in the first place. Machines only understand numbers. Well, more precisely, 1s and 0s, but let’s not go into binary encoding today. This section is about the transformer architecture mentioned in the definition of how an LLM works.

Vector embeddings are a numerical representation of a word in a multi-dimensional space. You can think of an embedding as a point on a map, a vector. The map isn’t your typical 2D map of Disneyland. It has more dimensions than we can comprehend. The best analogy is to think of this space like a multi-tiered chessboard.

The newest embedding technology can encode context. This means that the word “bank” in “bank robber” will occupy a different point on a map than the word “bank shot.” What embeddings allow machines to do is measure the distance between a pair. Thus, they can understand mathematically the similarity of the pair.

Embeddings allow machines to compare words mathematically and, more practically, entire sentences.

Semantic Triples

Machines have many ways to organize information. One of those ways that is common today is called a knowledge graph. This is a system that links different subjects and objects together in a structured format. The semantic triple is a fundamental building block or method for machines to organize pieces of information in a knowledge graph. I like to think of the triple as the nucleus of an atom. It’s the core and simplest form of how items connect.

Here’s what it looks like:

Subject -> Predicate -> Object

Subject = What we’re talking about

Predicate = The relationship that the subject has with the object

Object = A feature of the subject

For example, “Jordan Stevens is a digital marketing consultant.” “Jordan Stevens” is the subject, the predicate is “is a,” and “digital marketing consultant” is the object.

Now, looking more technically, here’s how a machine might store information about the subject Barack Obama.

Barack Obama

position held: President

place of birth: Honolulu

spouse: Michelle Obama

member of political party: Democratic Party

And, in RDF code: 

@prefix wd: <http://www.wikidata.org/entity/> .
@prefix wdt: <http://www.wikidata.org/prop/direct/> .

wd:Q76 wdt:P39 wd:Q11696 ;   # Barack Obama → position held → President of the United States
       wdt:P19 wd:Q18094 ;   # Barack Obama → place of birth → Honolulu
       wdt:P26 wd:Q13133 ;   # Barack Obama → spouse → Michelle Obama
       wdt:P102 wd:Q29552 .  # Barack Obama → member of political party → Democratic Party

wd:Q18094 wdt:P131 wd:Q782 . # Honolulu → located in administrative territorial entity → Hawaii

Entities

An entity is a person, place, idea or thing that is associated with a subject. All of those items listed above about Barack Obama are entities of the 44th President of the United States (another entity).

Discovering all of the entities related to a subject is one of the first things you need to do to build a reliable AEO strategy.

Weak Ties and Strong Ties

Now that we understand semantic triples and entities, I want to introduce the concept of weak ties and strong ties. As you develop your AEO strategy, this concept is fundamental to improving the visibility of the entities you want to improve. This approach captures the fundamental structures of LLMs, specifically their probabilistic nature and the concrete data stored about a subject.

First, you need to create a list of all the entities associated with a subject. You can usually find all of the known entities using Wikidata, ChatGPT and other public sources. The trick is to uncover entities missing from the public data, especially those where you’d like a connection to be made. These missing entities will become what you optimize for.

Armed with the list of entities, you need to map the strength of the connection to the object of each entity. For example, going back to Barack Obama’s entities, being the President of the United States would be a strong connection, while speaking Indonesian would be weaker.

Two prompts can help you with this task:

1. List ten things that you associate with [subject]. Rank them in order from highest to lowest association. Do not search the internet.

2. What are ten lesser-known facts about [subject]?

Depending on the popularity of your subject, these prompts might come up empty or off target, so you’ll want to use some judgment here, but they should give you a strong indication of how an LLM perceives your subject. 

After reviewing the list of entities, you’ll have a map of the strengths of the ties for each entity.

How to Optimize Subject-Entity Ties and Improve Overall Visibility

To increase the likelihood of an entity being paired with a subject, you need to increase the number of times the subject is paired with an object. Subjects paired more often with an entity/object become strong ties and are more likely to be mentioned by an LLM when speaking about a topic.

Likewise, if you want an entity to be less pronounced by LLMs, you need to deemphasize that object. In other words, don’t talk about it. Like a poorly tied boat at a harbour, that entity will eventually float away.

For example, if we wanted LLMs to reference Barack Obama’s Indonesian language skills more prominently, we would need to plaster the Internet with the phrase “Barack Obama speaks Indonesian.” And, because most advanced LLMs are multimodal, we’d want to include audio and video examples of his ability.

How does voice search impact AEO strategies and content structuring?

Voice search impacts AEO by prioritizing conversational queries and natural language. Structure content using concise answers, question-based headers, and schema markup. Target long-tail keywords and include direct, spoken-style responses to align with how users speak rather than type.

How should brands monitor and refine their AEO tactics for evolving AI and LLM search environments?

Brands should monitor and refine AEO tactics by tracking voice and AI-driven queries, analyzing SERP features, and updating structured data. Adjust content to match evolving natural language patterns used in LLMs. Use tools like Search Console, schema validators, and query data to test, adapt, and optimize consistently.

Can knowledge graph optimization help improve brand authority in zero-click search results?

Knowledge graph optimization improves brand authority in zero-click search results by enhancing entity recognition, increasing visibility in panels, and reinforcing brand associations. Implement structured data, manage entity profiles, and ensure consistent brand mentions across trusted sources to strengthen credibility and control over branded queries.

What is the role of semantic clustering and query expansion in optimizing content for LLMs?

Semantic clustering and query expansion optimize content for LLMs by grouping related topics and expanding keyword coverage. Clustering organizes content by intent, while query expansion addresses varied search phrasings. This alignment improves relevance, helps LLMs infer meaning, and increases visibility across semantically related queries.

Wrapping Up

Search is no longer about who ranks first in a list of links. It is about who delivers the clearest, most direct answer in the moment. As LLMs and answer engines continue to shape the way people find information, the businesses that adapt will gain visibility, authority, and customer trust. Those who hesitate risk being left behind.

AEO has become a critical part of digital strategy. It ensures your content is the one chosen, displayed, and spoken back to your audience. From voice search to zero-click results, the brands that prepare now will be the ones shaping the conversation tomorrow.

If you are ready to ensure your business remains visible in this new landscape, start today. Connect with an AEO Specialist and turn AI-driven search into a competitive advantage for your brand.