Tag

Content Optimization

Retrieval Chunking Architecture

AI engines retrieve passages, not pages. This article explains retrieval chunking architecture: the technical, learnable discipline that makes brand content extractable by AI engines. We cover definitional ledes, prompt-shaped headings, extractable tables, evidence-summary blocks, and FAQ structures, outlining how these elements improve AI retrieval and synthesis quality. The article also details common chunking failures and provides an implementation checklist for optimizing editorial pages.

Ronn Torossian · Jun 14, 2026

AI Communications

AI Communications vs GEO vs SEO

This article clarifies the differences between AI Communications, Generative Engine Optimization (GEO), and Search Engine Optimization (SEO), explaining their unique metrics, timelines, and how they fit into a strategic hierarchy. It emphasizes the importance of understanding each discipline to effectively allocate marketing budgets and achieve optimal content visibility across different platforms, including traditional search engines and AI answer engines.

Ronn Torossian · May 23, 2026

Generative AI

Signal Over Noise: How Unilever Uses AI to Make Social Media Actually Work

How Unilever built AI signal processing into social media at scale — turning content noise into precise, data-driven systems. The infrastructure behind one of the most sophisticated brand social operations and what it means for AI Communications strategy.

EPR Editorial Team · May 3, 2026