Which AI ranking signals does Google use? 

We finally have an official answer to this question. 

By unpacking Google’s Enterprise Tools, like the documentation behind its Vertex AI Search product, we discovered seven core trust signals its system uses to rank AI answers. 

What’s even better is that other popular generative AI platforms use the same signal stack. 

In other words, whether you’re optimizing for ChatGPT, Perplexity, or Google’s AI Overviews, they all use the same trust signals to determine which brands get cited and which get ignored. 

We also discovered that small businesses can improve most of these signals, a few of which you can start influencing immediately. 

In this post, we’ll break down all seven trust signals and tell you how to optimize for them. Keep reading to learn how to crack the AI search code. 

The Seven AI Ranking Signals Explained Simply 

Modern generative AI systems don’t use one magic ranking formula to pull online content. Instead, they engage in a seven-step process to ensure the content they cite:

1. Actually answers the user’s query 
2. Contains helpful, relevant information 
3. Comes from a trustworthy business
4. Uses the same general terms as the prompt
5. Is popular with other users 
6. Contains up-to-date information 
7. Aligns with general business rules and trust policies 

These signals ensure that AI platforms like Google’s AI Overviews consistently cite trustworthy, fresh content. 

The best part?

You don’t need any advanced AI knowledge to learn how to optimize for these signals. 

They’re basically just modern safeguards that ensure that AIs cite the type of content good businesses have always produced; content that’s clear, helpful, engaging, trustworthy, and up-to-date. 

Also, despite what many marketers thought up until this point, AI trust signals DO NOT replace traditional SEO signals; they exist alongside them. 

In that sense, AI search platforms use a hybrid of classic and advanced ranking signals. 

Here’s a closer look at each of the seven AI ranking signals (explained in plain English). 

#1 Relevance: Core algorithm ranking 

The first signal is relevance, which also goes by the name base ranking. 

This layer closely resembles classic search engine logic, but with an AI twist. While it still uses things like keyword matching and basic document retrieval principles, it’s also capable of understanding meaning, concepts, and synonyms. 

Think about it like this: the skeleton is classic SEO, but the muscles are modern and powered by AI. 

Base ranking is the search system’s first pass, and it does the grunt work of:

1. Retrieving content that’s most likely to answer the user’s question 
2. Combining traditional keyword scoring with modern semantic signals 
3. Feeding the ‘content candidates’ to the more advanced AI layers that follow 

#2 Topic clarity: Understanding the meaning behind the words

The second signal is topic clarity. This is where the AI determines what your page is actually about, even if you didn’t use perfect keyword matches.  

When the AI indexed your site’s content, it broke everything up into small chunks (usually 300 - 500 tokens each). It then converts these chunks into a numerical “meaning map” of sorts. These maps are called embeddings, and they help AI systems understand the overall topic of your page without relying on keywords. 

During the search process, the system gives each chunk a Gecko score, which measures how close the meaning of your content matches the meaning behind the user’s question. 

If the two line up closely, the system sees the content as highly relevant, even if the phrasing was different. 

#3 Keyword match: Old-school keyword matching 

Even though keyword matching is a classic search signal, it occurs after base ranking in the pipeline. 

Why?

It’s to ground the results with keyword relevance. 

As sophisticated as semantic retrieval can be, it lacks precision at times and can drift into ‘close, but not quite relevant’ territory. For this reason, AI systems use classic keyword matching to:

• Prevent semantic drift (i.e., venturing too far off topic) 
• Ensure the results answer the actual query and not something similar 
• Ground the AI’s interpretation back to the language the user actually used 

To be clear, keyword matching (BM25) exists as a supporting signal and not a primary one. Its job isn’t to choose which content to cite; it’s there to refine the results the AI already selected. 

#4: Engagement signals: How helpful is the content?

The engagement layer evaluates how likely users are to click on your result and whether or not they’ll be satisfied with it. 

This is handled by PCTR (predictive click-through rate), which approximates user satisfaction through patterns seen across millions of interactions.

The ultimate goal of this layer is to reward content that people actually like.

It evaluates things like:

• How often people click on results like yours
• Whether users will actually stay and keep reading
• How well your layout performs across mobile and desktop 
• How clear and compelling your snippets are (on average)  

If users historically engage well with your site’s content, AI systems will mark your content as more appealing. 

But if people frequently bounce due to slow load times, unclear snippets, or cluttered design, the system could lower your ranking or deprioritize your content. 

#5: Freshness: Does the content contain the latest information?

When topics change quickly, the newer content always wins in AI search.

This is because AIs understand when timing matters for certain topics (i.e., prompts with temporal intent). 

AI systems will check your site’s structured data and timestamps to ensure the freshness of your pages for these types of queries. 

Freshness matters for every niche, but it’s especially important for subjects that evolve fast, like news, events, prices, and trends. 

#6: Trust and authority: Whether your website is reliable or not 

AI search systems only want to cite reliable, well-made content. To do so, they evaluate quality indicators based on user behavior and site reputation. 

These include:

1. Third-party brand mentions (news sites, media outlets, relevant blogs, industry roundup articles, etc.) 
2. User reviews and reputation signals (third-party reviews, Google reviews, community forum discussion, etc.) 
3. Author signals and expertise 
4. Content quality, depth, and factual accuracy 
5. Brand coherence (naming consistency, stable phone/address/email, etc.) 

#7: Business rules: Additional rules based on business policies 

The final ranking layer contains a set of extra rules that search systems use to improve results or ensure safety. 

Based on a combination of business rules and safety overrides, the system may choose to boost some sources and bury others. 

It boosts:

1. Business partners
2. Sources with strong reputations 
3. Official health, finance, and legal sources 
4. Recently updated, technically healthy pages 

And buries:

1. Obvious spam and auto-generated junk content 
2. Low-trust or misleading articles 
3. Safety policy-violating content (like dangerous medical advice or explicit harassment) 

As long as you produce high-quality content that’s frequently updated and genuinely helpful, you shouldn’t have trouble making it past this trust layer.    

What are the AI Ranking Signals Businesses Can Influence Immediately?

Here are the signals that you can influence the quickest and easiest:

• Topic clarity – Content that follows a logical, airtight structure is easier for AI systems to parse and understand. Remember, AIs ingest your content in 300 - 500 token chunks, so you should break up your pages into hyper-focused subheadings that do not drift off topic. 
• Relevance – Accurate headings (especially H1s) containing exact-match keywords improve relevance and retrieval. 
• Freshness – Frequent content updates (including schema and timestamps) improve freshness. Don’t forget to update all your content, even your evergreen pieces. 
• Trust – Authoritative backlinks, strong user reviews, and positive third-party online mentions increase trust and help AI systems associate your brand as a reliable source for your area of expertise. 

Which signals aren’t as easy to influence?

Some of the AI ranking signals aren’t as easy for small businesses to influence, since they have very little control over them. 

These include:

• Personalized engagement data (based on the user’s preferences and location) 
• Long-term user patterns (what large groups of people search for) 
• Model-level decisions made inside AI systems (how they interpret meaning, safety filters, other preferences, etc.) 

These signals still matter, but they’re not something that you can ‘optimize’ in the traditional SEO sense. They’re mostly shaped by user behavior and the internal workings of AI models. 

How The HOTH Improves the Controllable AI Ranking Signals 

We’ve redefined some of our core offerings and even added a few products to include AI search optimization. 

Now, our managed SEO campaigns focus on improving structure and technical clarity through:

1. Logically-paced content 
2. Chunkable snippets 
3. Proper use of structured data 

Also, our link-building campaigns prioritize editorial quality, relevance, and context above all else. 

Lastly, our blog services provide the necessary depth to achieve topical authority with popular AI search systems like ChatGPT and Perplexity. 

If AI systems view your brand as an authority figure in your niche, they’ll frequently cite your content whenever users have questions about your area of expertise. 

Is better AI visibility your overall goal?

Our newly launched AI Discover service is all about improving your brand’s visibility on AI search platforms, so don’t wait to sign up! 

Final Thoughts: The Seven AI Ranking Signals 

To summarize, modern AI systems use a combination of classic and AI-powered trust signals to rank content. 

From our unpacking of Google’s Enterprise Tools, our most important finding was the importance of clarity to AI search systems. 

They rely on well-structured sections with content broken into clear ideas. Poor structure causes ranking instability in AI answers, which isn’t what you want.

Are you ready to improve your website’s ability to get cited by AI search tools?

Book a free strategy call with our expert team to discover the best path forward for your brand!