---
id: "prereq-llm-parsing"
type: "prereq"
source_timestamps: ["\\\"§ 1. Structure your content for machines", "not just humans.\\\""]
tags: ["ai-mechanics", "search"]
related: ["contrarian-seo-vs-geo"]
reason: "Necessary to understand why evocative marketing copy fails in agentic commerce."
source_url: "https://hbr.org/2026/02/how-brands-can-adapt-when-ai-agents-do-the-shopping"
source_title: "How Brands Can Adapt When AI Agents Do the Shopping"
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-ext-14-brands-adapt-ai-shopping"
sourceUrl: "https://hbr.org/2026/02/how-brands-can-adapt-when-ai-agents-do-the-shopping"
sourceTitle: "How Brands Can Adapt When AI Agents Do the Shopping"
---
# LLM Data Parsing vs. Traditional SEO

**Prerequisite.** The source assumes a basic understanding of how **Large Language Models (LLMs)** ingest and process **structured text and numbers**, as opposed to how traditional search-engine crawlers index keywords or how humans process visual branding.

**Why it's required:** it is necessary to understand why **evocative marketing copy fails** in agentic commerce — the crux of [[contrarian-seo-vs-geo]] and the motivation for [[concept-generative-engine-optimization-d14]] (see the quote [[quote-digest-text-numbers]]).

> **Enrichment note.** Adjacent knowledge a domain expert would bring: how generative/answer engines weight structured data and factual clarity over keyword density, and how schema markup / product feeds already influence AI-mediated discovery — the technical substrate beneath the GEO framing.


## Related across articles
- [[prereq-llm-rag-mechanics]]
- [[prereq-structured-data]]
- [[prereq-pim-systems]]