When an AI ingests your content, it often breaks it down into “chunks” before embedding them into vector space. If your chunks are too large, context is lost. If they are too small, meaning is fragmented. So, what is the optimal length?

The 512-Token Rule

Many popular embedding models (like OpenAI’s older text-embedding-ada-002) had specific optimizations around 512 or ~1000 tokens. While newer models like gpt-4o support 128k+ context, retrieval systems (RAG) often still use smaller chunks (256-512 tokens) for efficiency and precision.

The landscape of Search Engine Optimization (SEO) is undergoing a seismic shift. For decades, the primary mechanism of discovery was the keyword—a string of characters that users typed into a search bar. “Best shoes.” “Plumber NYC.” “Pizza near me.”

Today, with the advent of Large Language Models (LLMs) and vector databases, we are moving towards an era of contextual vectors.

The Vectorization of Meaning

In traditional SEO, matching “best running shoes” meant having those words on your page in the <title> tag and <h1>.