The Mathematics of Semantic Chunking: Optimizing Retrieval Density

In the frantic gold rush of 2024 to build Retrieval-Augmented Generation (RAG) applications, we committed a collective sin of optimization. We obsessed over the model (GPT-4 vs. Claude 3.5), we obsessed over the vector database (Pinecone vs. Weaviate), and we obsessed over the prompt.

But we ignored the input.

Most RAG pipelines today still rely on a primitive, brute-force method of data ingestion: Fixed-Size Chunking. We take a document, we slice it every 512 tokens, we add a 50-token overlap, and we pray that we didn’t cut a critical sentence in half.

For 20 years, the “Sitemap” has been the standard for indexing. You create a list of URLs, you tell the search engine where it is, and then you wait. you expect the crawler to come back… eventually.

In the Agentic Web, “eventually” is too slow. News breaks in seconds. AI models update in real-time. If your content isn’t indexed now, it might as well not exist.

Enter IndexNow, an open protocol championed by Microsoft Bing and Yandex.

HTML is for browsers; Markdown is for brains. LLMs are trained heavily on GitHub repositories, StackOverflow, and technical documentation. This makes Markdown their “native” format. They “think” in Markdown.

Token Efficiency

Markdown is less verbose than HTML.

• HTML: <h1>Title</h1> (9 characters, ~3 tokens).
• Markdown: # Title (7 characters, ~2 tokens).
• HTML List: <ul><li>Item</li></ul> (21 characters).
• Markdown List: - Item (6 characters).

Across a 2,000 document, this saves thousands of tokens. A clean Markdown file consumes fewer tokens than its HTML equivalent, allowing more content to fit into the context window.

We have spent the last decade complaining about “Crawled - currently not indexed.” We treat it as a failure state. We treat it as a bug.

But in the Agentic Web of 2025, “Indexation” is not the goal. “Retrieval” is the goal.

And paradoxically, to maximize Retrieval, you often need to minimize Indexation.

The Information Density Argument

LLMs (Large Language Models) and Search Agents operate on Information Density. They want the highest signal-to-noise ratio possible.

As search moves towards “Answer Engines,” users are demanding not just relevance, but safety. They (and the agents acting on their behalf) want to know where products come from.

The Rise of Ethical Ranking

We predict that future ranking algorithms will incorporate Supply Chain Provenance as a major signal for e-commerce.

• Opaque Supply Chain: Lower trust score.
• Transparent Supply Chain: Higher trust score.

Data Provenance via AEO

Displaying your Authorized Economic Operator (AEO) status proves you are a verified, low-risk international trader. When an B2B procurement agent scouts for suppliers, it will filter results. Query: "Find 5 reliable steel suppliers in Germany." The agent checks for:

Just as a grounding wire directs excess electricity safely to earth, Schema.org markup directs model inference safely to the truth.

In the chaotic world of unstructured text, hallucinations thrive. “The CEO is John” might be interpreted as “The CEO dislikes John” depending on the sentence structure. But Structured Data is unambiguous.

The Semantic Scaffold

"employee": {
  "jobTitle": "CEO",
  "name": "John"
}

There is no room for hallucination here. The relationship is explicit.

In the rush to build “AI-Powered” search experiences, engineers have hit a wall. They built powerful vector databases. They fine-tuned state-of-the-art embedding models. They scraped millions of documents. And yet, their Retrieval-Augmented Generation (RAG) systems still hallucinate. They still retrieve the wrong paragraph. They still confidently state that “The refund policy is 30 days” when the page actually says “The refund policy is not 30 days.”

Why? Because they are feeding their sophisticated models “garbage in.” They are feeding them raw text stripped of its structural soul. They are feeding them flat strings instead of hierarchical knowledge.

In the cutthroat world of legal marketing—where “Personal Injury Lawyer” CPCs can rival the GDP of small nations—finding an untapped channel is the holy grail. For the last six months, a quiet battle has been waging among the tech-savvy elite of the legal sector. The battleground is not Google. It is not Bing. It is Grokipedia.

You asked a critical question: “Is Grokipedia something I should be targeting or utilizing to build authority?”

There is a dirty secret in SEO that engineers at Google vehemently deny but data scientists quietly confirm: User Engagement is a Ranking Factor.

But in 2025, it is more than a ranking factor. It is an Indexing Factor.

When your page is stuck in “Crawled - Currently Not Indexed,” it usually means Googlebot has processed the content and found it technically sound but behaviorally suspect. The algorithm asks: “If I index this, who will click it?”