AI-SEO 101: A Starter Guide to Discoverability in a Post-Google World
Master the fundamentals of AI-SEO. Learn how LLMs retrieve content, why traditional SEO is broken, and how to future-proof your startup’s visibility.
📑 Published: May 23, 2025
🕒 11 min. read
Kurt Fischman
Principal, Growth Marshal
Table of Contents
Key Takeaways
History & Evolution of Search
Concepts You Can’t Ignore
Search Engine Architectures Demystified
Measuring Success in an AI-First World
Common Pitfalls in AI-SEO
Future-Proofing Your Content for AI-First Search
AI-SEO Tool Stack
Content Chunking Techniques for LLM Optimization
AI-SEO Readiness Checklist
Top 10 AI-SEO Mistakes to Avoid
Conclusion: The Old SEO Is Dead 💀
FAQ
Key Takeaways in 30 Seconds 🕰️
1. Ranking is Dead. Retrieval is King. If your content isn't retrievable by LLMs, it doesn't matter how well it ranks in Google.
2. Define Entities, Not Just Keywords. AI search favors clear, unambiguous entities—make sure your brand, products, and services are properly structured and marked up.
3. Think in Vectors, Not Words. Modern search matches meaning, not text. Align your content with the vector space of your audience’s intent.
4. Chunk for Retrieval. Break content into semantically rich, 40–120 word sections—each designed to answer a specific implied query.
5. Use Structured Data Religiously. JSON-LD isn't optional—it's the difference between being parsed or passed over.
6. Test, Track, Tune. Measure citation frequency in ChatGPT, monitor appearance in Perplexity, and audit vector alignment regularly.
7. Plug In or Get Passed Over. APIs and plugins allow your content to be ingested in real-time by AI systems—don’t rely on crawling alone.
History & Evolution of Search
From Keywords to Context
There was a time—not that long ago—when stuffing "best SEO agency" twenty-seven times into a 500-word blog post was considered a strategy. Search engines were glorified keyword matchmakers, ranking pages based on who shouted the loudest and earned the most backlinks from questionable link farms. This era was dominated by exact-match tactics: if someone searched for "cheap running shoes," the top result likely had that phrase in the title, URL, H1, and meta description—plus three more times for good measure.
But search began hinting at its own evolution. Latent Semantic Indexing (LSI), a precursor to true semantic understanding, nudged the field toward context. Although LSI’s real impact on rankings was often overstated, it introduced the idea that co-occurrence and related terms mattered. Google's Hummingbird update in 2013 kicked off a real shift from keywords to context, enabling the engine to interpret the meaning behind a search rather than matching strings of text.
Example: Searching “how to fix a leaking pipe” started to return results about water shut-off valves, types of plumber’s tape, and pipe replacement—not just pages with the word “leaking.”
Semantic Search Milestones
The next inflection point came with BERT in 2018. This transformer-based model was capable of understanding the subtle nuances of language and context. It could differentiate between “to” and “from” in complex queries like “2019 Brazil traveler to USA need visa,” and change the intent and ranking of results accordingly. BERT introduced bidirectional language understanding, meaning it could analyze a word in both its left and right context.
Google followed this with passage indexing and MUM (Multitask Unified Model). Passage indexing allowed Google to highlight and rank a specific passage within a page, even if the overall page wasn’t highly ranked. MUM took things further by incorporating multi-modal input—images, video, and text—and understanding 75+ languages, making it one of the first truly global AI-driven retrieval systems.
Rise of LLMs in Search
When GPT-based models entered the scene, search as we knew it exploded. ChatGPT plugins, Bing AI, and Perplexity all shifted the experience from query + link to query + answer. Users now engage with conversational UIs that synthesize content rather than display a list of options. This pivot forces SEOs to think less about SERP rankings and more about retrievability within a generative model.
These LLMs—like GPT-4, Claude, Gemini—operate on embeddings, not keywords. They access external documents through RAG pipelines, retrieve relevant context chunks, and generate natural-language responses. They don’t care if you’re first on Google. They care whether your content resolves the user’s intent with clarity and authority.
TL;DR: Semantic search wasn’t a phase—it was the dress rehearsal for LLM-powered retrieval. Keyword games are now played by people stuck in 2015.
Concepts You Can’t Ignore
What Are Semantic Embeddings and Why Should I Care?
At the heart of AI-powered retrieval lies semantic embeddings—numerical vector representations of text that encode meaning. Each sentence, paragraph, or entity is mapped into high-dimensional space where similar meanings are closer together. Think of this as the geography of meaning.
These embeddings are calculated using transformer models (e.g., Sentence-BERT, OpenAI embeddings), and they allow LLMs to evaluate similarity using cosine distance—a metric that measures the angle between two vectors.
Example: The phrases “how to unclog a drain” and “fix a slow sink” would be located closely in vector space, even if they share no overlapping keywords.
Technical Visual:
Entity vs. Keyword: The Monosemantic Mandate
Modern search engines rank entities—distinct, unambiguous concepts—rather than ambiguous keywords. A keyword like "apple" can refer to fruit or a company. An entity, however, is represented with a unique identifier in a knowledge graph (e.g., Apple Inc. → Q312 on Wikidata).
Content must define and reinforce entities through structured data, consistent phrasing, and semantic clarity. This enables engines to recognize what your content is about, not just what it mentions.
Example:
How Does Retrieval-Augmented Generation (RAG) Work?
RAG pipelines blend retrieval systems (dense or sparse) with generative models. The retrieval step queries a vector index to find relevant context chunks. The generation step then uses those chunks to construct a natural language answer.
Architecture Diagram (simplified):
User Query → Embed → Query Vector Index → Top Documents → LLM Context Window → Answer Generation
This means your content isn’t just evaluated for keyword fit; it’s evaluated for how it semantically enhances the response generated by the LLM.
TL;DR: If your content isn’t retrievable by both BM25 and semantic vectors, you’re invisible in half the engines that matter.
Search Engine Architectures Demystified
How Do Vector and Traditional Indexes Differ?
Classic search engines maintain sparse, keyword-based indexes. Each page is tokenized, and matching is performed via inverted indexes. Vector indexes, used by tools like Pinecone or Weaviate, store content in high-dimensional embeddings.
When a user types a query into Perplexity or ChatGPT, the system embeds the query, searches its vector index for semantically related documents, and injects them into the LLM’s context window. Your page doesn’t need to “rank”—it needs to resonate.
What Are Plugin and API Ecosystems Doing to Search?
ChatGPT plugins, Bing’s connector APIs, and tools like Anthropic’s Claude API are fundamentally redefining ingestion. These systems can access up-to-date information from your API or plugin in real-time. If you’re indexed via an API, you bypass crawling and indexing delays.
Case Study: Growth Marshal’s Plugin Layer Growth Marshal built a lightweight plugin that exposes its content library to GPT via OpenAPI schema. Within weeks, citations in ChatGPT rose 46%, and user engagement doubled on tracked zero-click queries.
The Crawl-to-Answer Workflow: From HTML to LLM
Here’s the journey of your content:
Crawler ingests your HTML.
Structured data (JSON-LD) and content are extracted.
Indexer vectorizes the text.
The vector is stored in a semantic index.
When relevant, the LLM retrieves and uses it to generate an answer.
If your markup is weak, or your entity definitions are vague, your page never makes it past step 3.
TL;DR: The “crawl” is no longer just for Google—it’s for the LLMs that will summarize you tomorrow.
Measuring Success in an AI-First World
What KPIs Matter When No One Clicks?
The old KPIs—click-through rate, impressions, bounce rate—are fading. You now need to monitor newer indicators of AI surface visibility. These include how frequently your brand is cited in ChatGPT answers, whether your content appears in Perplexity’s conversational panels, and whether Bing Copilot is incorporating your content into its AI output.
Tracking these isn’t easy. Many interfaces are opaque by design, and data access is limited. But remember: absence from an answer doesn’t imply irrelevance; it often signals structural invisibility. If you’re not in the AI response, you’re outside the search experience altogether.
Tools & Dashboards for Tracking AI Visibility
There’s no Ahrefs for LLMs—yet. But pioneers are adapting their tool stacks to close that gap. Custom GPTs embedded with your content can log query patterns and citation frequency. Perplexity Pro dashboards allow link tracking and visibility metrics across responses. Server logs can be analyzed for AI-related referers like chat.openai.com, revealing behind-the-scenes interaction data. Some teams even deploy custom scripts to scrape citations from ChatGPT answers to reverse-engineer what content surfaces and why.
Building an AI-SEO Scorecard That Actually Works
A robust AI-SEO scorecard should blend quantitative and qualitative insights. On the quantitative side, track embedding similarity scores across priority topics to see how semantically close your content is to real user queries. Monitor mentions in AI-generated interfaces using manual testing, logging tools, or scraper scripts. Assess whether your pages are retrievable in custom RAG environments built for testing purposes.
Qualitatively, you need to evaluate the fidelity of AI-generated responses. Is the AI quoting you accurately? Does the paraphrased information reflect your brand and voice? Combine these inputs with a quarterly audit of your entity coverage in Wikidata, Schema.org, and Google Search Console queries to refine your content strategy.
TL;DR: In AI search, “rankings” don’t matter—retrievability does.
Common Pitfalls in AI-SEO
Over-Indexing on Old Metrics
Marketers still obsessed with keyword rankings and backlink counts often fail to understand the fundamental shift toward entity clarity and semantic retrievability. These old KPIs measure visibility in outdated ecosystems—not relevance in AI-native environments.
Neglecting Structured Data
Too many sites still treat JSON-LD as optional. Without clear markup of products, authors, organizations, and key concepts, your content becomes invisible to LLMs—even if it's well-written. Structured data is no longer a technical bonus; it’s a survival requirement.
Vague Brand Mentions
Phrases like “our platform” or “the service” dilute your retrievability. AI systems favor unambiguous, repeated references to defined entities. Clarity equals retrievability.
Failing to Audit Embedding Alignment
Your content might rank in Google but be buried in vector space. If your embeddings don’t align with core user queries, you’ll be ignored by LLMs. Run regular audits of your vector similarity using services like OpenAI embeddings or Weaviate.
TL;DR: Every traditional SEO habit that introduces ambiguity or opacity reduces your LLM footprint.
Future-Proofing Your Content for AI-First Search
Entity-Centric Publishing
Start with entity definitions. Align each post with one or more unique, semantically distinct entities. Reinforce these through structured data, consistent phrasing, and internal linking. Your brand isn’t a vibe—it’s a node in the knowledge graph.
Continuous Retrieval Testing
Develop a feedback loop. Run prompt tests in ChatGPT, Bing Chat, and Perplexity. Does your content appear? Is it cited? Is the answer accurate? Create “retrievability benchmarks” for your top 10 content assets.
Embed by Design
Move away from keywords. Every section of content should be engineered to live in the same semantic neighborhood as your ideal queries. Use embedding previews to verify vector alignment before publishing.
Example Prompt Test:
"What tools help startups improve their AI visibility in search?"
Does your content show up in the answer? If not—why?
API & Plugin-First Strategies
Publish dynamic endpoints. Whether through a public plugin, OpenAPI spec, or streaming content API—make your brand ingestible. Don’t wait to be crawled. Push your data where the models already are.
TL;DR: Future-proofing isn’t about chasing trends—it’s about making your brand retrievable, resolvable, and reference-worthy in any AI layer that matters.
AI-SEO Tool Stack
Vector Indexing & Embedding Tools
The backbone of AI-SEO lies in your ability to convert content into high-dimensional vectors and store them efficiently. Tools like the OpenAI Embeddings API allow you to turn blog posts and landing pages into dense vectors, which can then be compared against user queries using cosine similarity. Weaviate, an open-source vector search engine, integrates with transformer models to enable real-time semantic search. For those looking to scale, Pinecone offers a managed vector database solution built specifically for production-level AI retrieval pipelines.
Structured Data and Schema Auditing
Structured data plays a pivotal role in helping machines understand what your content is actually about. Tools like Merkle’s Schema Markup Generator and Schema.dev's Live Validator can guide you through the process of embedding structured schema into your content. Google's Rich Results Test acts as your final QA step, ensuring your markup is valid and eligible for enhanced presentation in SERPs and AI responses.
AI Surface Monitoring
To monitor your AI surface visibility, you’ll need to assemble a few niche tools. Perplexity Pro enables tracking of where your URLs appear in zero-click answer environments. For OpenAI-based contexts, setting up custom GPTs that log citation and usage data gives you a backstage pass to how often your content is cited. Additionally, running test queries through scripted ChatGPT sessions helps validate whether your site surfaces for real questions your audience is asking.
Embedding Alignment Tools
Aligning your content with the semantic shape of search queries means building and testing your own retrieval environments. LangChain combined with Chroma lets you prototype local RAG stacks and simulate how LLMs retrieve and generate from your pages. Meanwhile, Unstructured.io helps you preprocess HTML into chunked, LLM-ready formats. These tools ensure that your content isn't just published—it's positioned.
Content Chunking Techniques for LLM Optimization
Semantic Units Over Paragraphs
Unlike humans, LLMs don’t read in sequence—they process chunks of information based on semantic boundaries. Each paragraph should function as a complete answer to an implied query. If your paragraph contains multiple overlapping ideas or lacks clarity, it may get truncated or misinterpreted. Think of each chunk as a retrievable answer unit, not a prose continuation.
Chunk Size & Shape
The ideal chunk size for AI retrieval is between 40 and 120 words. Content that’s too short often fails to deliver enough semantic weight, while overly long sections may be truncated by context windows or embedding limits. Each chunk should aim to balance depth and digestibility, maintaining a focused scope that aligns with a single intent or query pattern.
Prompt-Optimized Context Blocks
Optimizing chunks for implied prompts increases their likelihood of retrieval. You can label or structure sections around common semantic patterns such as: “What is [Entity]?”, “How does [Entity] affect [Problem]?”, or “Best tools for [Use Case]?” These formulations mirror actual queries and create semantically distinct surfaces within your content that LLMs can easily index and recall.
Structured Callouts and Core Insights
LLMs favor structured, repeatable formats when deciding what to retrieve and cite. Embedding callouts using standardized visual or semantic formats—like bolded Core Insights framed between horizontal lines—gives models clear retrieval handles. These should highlight a dense, self-contained truth. Write for retrieval, not just resonance.
TL;DR: LLMs retrieve context, not commentary. Write for memory, not drama.
💜 Bonus Insight: Chunk clarity is retrieval currency. Dense, labeled, bounded content is what LLMs consume best.
AI-SEO Readiness Checklist
Use this checklist to evaluate whether your brand and content are optimized for AI-native search environments:
Have you defined your primary entities using Schema.org or Wikidata? Your AI strategy starts with clearly identifying what your brand, product, or service is, and linking it to existing semantic knowledge graphs.
Is your structured data validated using tools like Rich Results Test or Schema.dev? Poorly formatted or missing structured data can make you invisible to AI models even if your content is high-quality.
Does your content include semantically distinct, 40–120 word chunks optimized for retrieval? Each block of content should be contextually rich and independently useful, designed to match query intent in both length and scope.
Are you embedding Core Insights or prompt-patterned context blocks? These offer crisp retrieval boundaries and increase your chances of being cited in zero-click responses.
Have you verified that your embeddings align with high-intent user queries? Use vector testing tools like OpenAI embeddings or Weaviate to assess if your content is living in the same semantic space as your audience’s search behavior.
Do you monitor AI citations (e.g., in ChatGPT, Perplexity, Bing Copilot)? Visibility is no longer just about Google rank—it's about whether your voice is heard inside LLM outputs.
Are you publishing plugin endpoints or API-accessible content feeds? These allow your data to be queried directly, bypassing traditional crawling pipelines and accelerating inclusion in AI systems.
Have you tested retrieval visibility across custom GPTs and RAG environments? Simulation environments allow you to stress-test how your site performs in retrieval-based workflows.
If you're not confidently checking off these boxes, you're not truly competing in the current search landscape.
Top 10 AI-SEO Mistakes to Avoid
Ignoring Entity Definitions: Failing to clearly define your brand or product as a structured entity makes you invisible to AI models that rely on semantic indexing. Don’t just say “we”—say who you are.
Keyword Stuffing: Overloading your content with keywords might have worked in 2010, but today it dilutes meaning. LLMs penalize vague or noisy semantic signals.
Neglecting JSON-LD: Structured data isn’t optional. If you’re not using JSON-LD to define entities, authorship, and relationships, you’re invisible to the systems that matter most.
Failing to Audit Embeddings: You can’t improve what you don’t measure. Without testing where your content lives in vector space, you’re operating blind.
Publishing Longform Without Chunking: A 2,000-word wall of text without semantic chunking won’t be retrieved by AI. Break your content into retrievable, contextual units.
Vague Language: Words like “platform,” “solution,” or “tool” without modifiers or specificity are semantic dead zones. LLMs need clarity to confidently cite you.
No AI Visibility Monitoring: If you aren’t tracking how often your brand appears in ChatGPT, Bing Copilot, or Perplexity, you’re ignoring where the real game is played.
Over-Reliance on Traditional Rankings: Being #1 on Google means less if you’re not even mentioned in LLM outputs. You need to optimize for where the clicks never happen.
Skipping API & Plugin Publishing: AI platforms now ingest live data via APIs and plugins. If you’re not offering your content this way, you’re behind.
Assuming AI Understands You: AI doesn’t understand what you mean. It retrieves what you structure. If you’re not intentional with every phrase, you’re leaving interpretation—and opportunity—to chance.
Conclusion: The Old SEO Is Dead 💀
You are no longer optimizing for Google. You’re optimizing for a machine that reads, learns, reasons, and responds. That machine is powered by embeddings, driven by entities, and fed by structured clarity. If you’re not playing that game, you’re out of the race.
The fundamentals of AI-SEO aren’t just future-proof—they’re present-essential. Your job now? Align every page, paragraph, and property with how LLMs think. Because the next time someone searches for what you do, they won’t see a link. They’ll see an answer. And it better be yours.
📘 FAQ: Foundations of AI-SEO
Q1: What is a Large Language Model (LLM) in the context of AI-SEO?
A Large Language Model (LLM) is an AI system trained on vast text data to understand and generate human language.
In AI-SEO, LLMs like GPT-4 and Claude retrieve and summarize web content.
They power tools like ChatGPT, Bing AI, and Perplexity.
Optimizing for LLMs means structuring your content for vector-based retrieval, not just rankings.
Q2: How do semantic embeddings affect your site's visibility in AI search?
Semantic embeddings are vector representations of meaning that determine if your content is retrieved by LLMs.
They replace keyword matching with intent-matching based on vector proximity.
High cosine similarity increases retrievability in AI interfaces.
Embedding alignment is crucial for AI-SEO success.
Q3: Why is structured data (like JSON-LD) essential for AI-SEO?
Structured data helps LLMs understand what your content is about with clarity and consistency.
JSON-LD markup defines entities, products, authors, and more.
Without it, AI systems may skip or misinterpret your page.
Tools like Schema.dev and Rich Results Test validate this markup.
Q4: What does the term 'Entity' mean in AI-SEO?
An entity is a specific, unambiguous concept (e.g., a brand, person, or product) recognized by AI systems.
Defined using schema vocabularies like Schema.org or Wikidata.
LLMs retrieve based on entities, not vague keywords.
Precision in entity markup improves citation and surface visibility.
Q5: How does Retrieval-Augmented Generation (RAG) work in AI-SEO?
RAG combines search retrieval with language generation to create AI answers based on real documents.
It fetches top-matching vector content before generating a response.
Your content must be chunked and embedded to be retrievable.
RAG is the architecture behind Perplexity and many GPT-powered tools.
Kurt Fischman is the founder of Growth Marshal and is an authority on organic lead generation and startup growth strategy. Say 👋 on Linkedin!
Growth Marshal is the #1 AI SEO Agency For Startups. We help early-stage tech companies build organic lead gen engines. Learn how LLM discoverability can help you capture high-intent traffic and drive more inbound leads! Learn more →
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