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Retrieval Chunking Architecture
AI Communications

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.

EPR Editorial Team ·
How to Correct AI Misinformation About Your Company
Crisis Communications

How to Correct AI Misinformation About Your Company

The article provides a playbook for correcting AI misinformation about your company. It outlines steps like verifying errors, finding sources, scoring source authority, issuing correct facts at higher authority, and re-testing. It also discusses ineffective methods like contacting AI companies directly or suing the model, emphasizing that building authoritative content is key to shaping AI outputs.

EPR Editorial Team ·