---
id: "action-tailor-to-llm-processing-styles"
type: "action-item"
source_timestamps: ["§ How to Market to LLMs"]
source_url: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
source_title: "Forget What You Know About Search. Optimize Your Brand for LLMs."
tags: ["optimization", "platform-strategy"]
related: ["claim-llm-processing-styles-vary"]
action: "Tailor content attributes to align with the specific processing styles and values of target LLMs."
outcome: "Maximized visibility and favorable sentiment on the most strategically important AI platforms."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-ext-10-optimize-brand-for-llms"
sourceUrl: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
sourceTitle: "Forget What You Know About Search. Optimize Your Brand for LLMs."
---
# Tailor content to specific LLM processing styles

**Action:** Recognize that different LLMs value different attributes (e.g., **Llama values 'uniqueness,' ChatGPT values 'local options,' Perplexity values 'flexibility'** — see [[claim-llm-processing-styles-vary]]). Tailor content to the nuances of the dominant model that best amplifies your brand's narrative strengths, while maintaining balance to avoid diluting impact across all models.

**Expected outcome:** Maximized visibility and favorable sentiment on the most strategically important AI platforms.

**Enrichment / caveat:** Balance model-specific tailoring against **brand consistency and operational complexity** — over-customization risks fragmentation and conflicting messages. Recommended sequence: build a core of **universal authority-first content** (depth, structure, third-party validation) *first*, then layer selective per-engine optimizations where ROI is clear.