Understanding how AI search works: A super basic rundown
We need to make our product marketing work harder in the age of AI but how do we do that when everything else was written to serve traditional SEO?.
Product marketing leaders face a critical challenge in 2025: differentiation.
As more B2B audiences turn to generative search platforms like ChatGPT, Gemini, and Perplexity for autonomous research, traditional ways of standing out are becoming less effective. It's projected that by 2027, 90 million online users in the U.S. will be using generative AI for search.
So the question I’m getting asked most frequently is “How do we ‘rank’ on these platforms like we have on Google with traditional SEO?”
Thing is, the way AI large-language models process your marketing content differs fundamentally from traditional search engines. While Google crawls your site continuously (and that’s also been changing constantly with their helpful content updates), AI systems build their understanding through periodic training cycles. This means your latest product documentation or feature updates follow a different path to visibility than what you’re used to with traditional SEO.
Additionally, when AI examines your content, it behaves more like a technical analyst than a search crawler. It segments your documentation into meaningful chunks, builds connections between features and use cases, and validates your claims against its broader knowledge base.
Let's look at HubSpot as an example - they've been in the spotlight recently over their dramatic blog traffic loss. Using a few of their top pages as an example, here's how AI search (content) processing would work👇🏽
Traditional search (SEO) vs. AI search (AIO)
Natural Language Processing (NLP) powers how LLMs understand and evaluate content. While traditional SEO focuses on keyword placement, like strategically sprinkling "cloud storage" throughout a page, NLP analyzes the natural patterns in how people actually write and talk about products. It's the difference between optimizing for search engines and writing for real humans.
NLP instead looks at the entire context:
What problems does your cloud storage solutions solve?
How does it integrate with other systems?
What technical requirements does it have?
Here's what this means for your product marketing content
First, technical accuracy matters more than marketing language
Instead of writing "revolutionary AI-powered insights," specify "machine learning models analyzing 50+ data points to predict customer churn with 92% accuracy."
The first version uses marketing terms that NLP systems can't validate. The second version provides specific capabilities that NLP can verify against technical documentation. It goes without saying, this approach prioritizes well-structured technical documentation over marketing claims but at the same time, strips your language of unique differentiators at times, in trying to standardize the information.
Second, context drives understanding
When you describe a feature, connect it to:
The technical problem it solves
How it actually works
Required technical specifications
Integration points with other systems
Third, consistent terminology beats creative variation
If your API documentation calls it "user authentication," don't call it "identity verification" in your marketing eBooks. NLP systems build stronger connections between content that uses consistent technical terms. Additionally, AI search maintains contextual awareness throughout user interactions, providing increasingly personalized results based on conversation history and user behavior patterns. The system can understand follow-up questions and maintain topic continuity.
How LLMs learn & update their knowledge
Coming back to those training cycles and how LLMs are different from the Google.com search experience of 2023 😅, Generative search tools don't automatically learn or update when new content is published online. Instead, they rely on two main mechanisms for accessing new information:
Model Updates
The underlying model parameters are usually updated during official training/retraining cycles by AI companies.
Continuous training and monitoring help keep models current, but this happens at the company level, not automatically.
Training involves adjusting the weights and parameters associated with tokens to minimize prediction errors.
Previous user interactions and feedback may inform future training but don't update the current model.
Web Access
LLMs like ChatGPT, Gemini, and Copilot can actively search the web for current information, while others like Claude cannot (yet).
Web-enabled LLMs use agents to crawl and parse web content
The process involves:
Crawling: Systematically exploring web pages
Parsing: Analyzing content structure
Extracting: Collecting relevant information
So what happens to my current (traditional SEO) content?
If there are inconsistencies between your marketing and technical content, you risk losing visibility in relevant ICP queries - even when your solution matches what they're looking for.
AI tends to standardize content presentation so unique differentiators can get lost in creative writing. AI systems don't just read your content but rather reconstruct it. When generating responses to user queries, AI combines information from your technical documentation, marketing materials, and its broader understanding of your market category.
This reconstruction process often strips away the unique positioning language that product marketers work so ardously on, standardizing feature descriptions and potentially missing crucial differentiators. The way forward lies in creating a bridge between your marketing narrative and technical reality if they’re not lining up already.
Your product descriptions must maintain consistency with API documentation.
Your use cases need validation through technical specifications.
Your integration requirements must match the actual implementation details.
Transforming product marketing: Building content strategy for both humans and AI
With 84% of buyers initiating first contact with their ultimate vendor choice, your content needs to work harder in generative AI search. This means getting technical, aligned, and super clear in your narratives. Your goal? Ensuring you make the shortlist during their autonomous research journey.
Begin with your product comparison pages. These pages typically excel at traditional SEO but often lack the structured data AI systems need to make accurate comparisons. For each feature comparison, provide both a marketing description and corresponding technical validation points.
Feature announcements require similar attention. Rather than simply describing new capabilities, frame them within their technical context. Include structured data that clarifies technical specifications, integration requirements, and API endpoints. This gives AI systems the concrete, verifiable information they need to accurately represent your product's capabilities.
Technical documentation becomes your source of truth. Every marketing claim should trace back to documented technical capabilities. When describing features as "enterprise-ready," specify exact scaling capabilities, uptime guarantees, and security certifications.
A modern product marketing content strategy requires:
First-party data and original insights to stand out
Integration of real user feedback and product usage data
Content that reflects actual product experience rather than just features
A new, growing perspective on search optimization
While traditional SEO expertise remains valuable, succeeding in 2025 requires mastering a dual approach that serves both search engines and AI systems effectively.
This transformation goes beyond keywords and rankings into building technically validated content that maintains its integrity across all search platforms.
Want to know exactly how to make this shift? In next week's edition of The 100, I’ll cover:
Building a technical foundation that serves both AI and humans when it comes to designating context and value to your content
Restructuring content responsibilities/teams for dual optimization
Creating measurement frameworks for traditional and AI search success
Developing validation processes that work across both systems
Until then, consider auditing one of your key product pages through both lenses - traditional SEO and AI search visibility. What differences do you notice? I'd love to hear your findings so I can share comprehensive insights from across our community in the next edition.
Want to go past the basics I speak to above? Here are a few resources you may find insightful.
Rand Fishkin’s take on how to optimize your content’s visibility on GPT
SemRush’s latest report: Investigating ChatGPT Search: Insights from 80 Million Clickstream Records
‘Why Triplicates Matter’ by Everette Sizemore (SEO and Content Strategist)
How to Optimize Your Website and Content to Rank in AI Search Results by Xponent21
Beyond SEO: Your Complete Guide to AI-First Content Optimization by totheweb
Optimizing Content for LLMs: Strategies to Rank in AI-Driven Search by PenFriend (this is a 20+min read!)
P.S. Fun fact: When I went searching for 'AIO' discussions on Reddit, I discovered it actually means 'Am I Overreacting' - not AI optimization as I'd hoped 🤦🏽♀️. Still joined the sub though! 🤭 For those interested in the intersection of AI search and traditional SEO, r/SEO is full of insights (some credible, some….you’d have to decide on your own). I particularly enjoy questions like "What do you say to a potential client who tells you 'I type my queries in Perplexity, not in Google'" - they give you a glimpse into how other practitioners and marketers are wrestling with these questions.
Till next week ✌🏼
Chae