---
id: "concept-resolution-optimization"
type: "concept"
source_timestamps: ["§ Probing the human-AI brand awareness gap", "§ How to increase brand awareness on 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: ["content-strategy", "llm-behavior", "user-intent"]
related: ["claim-llms-optimize-for-resolution", "action-identify-job-to-be-done"]
definition: "The principle that LLMs prioritize content that solves a user's specific problem or query, rather than content designed merely to capture attention."
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."
---
# Resolution Optimization

**Resolution optimization** is the central mechanical insight of the source. Traditional search engines and social-media algorithms are designed to optimize for **attention** — rewarding clicks, engagement, and sensationalism. LLMs, by contrast, are designed to optimize for **resolution** — providing the most accurate, comprehensive, and useful answer to a specific user prompt (see [[claim-llms-optimize-for-resolution]] and [[quote-resolution-over-attention]]).

For marketers this means shifting from persuasive, aspirational *broadcasting* to precise, solution-oriented *narrowcasting*. Content must clearly articulate the **'job to be done'** — linking product features to specific contexts, user needs, and proven outcomes rather than relying on vague marketing copy. The operational move is to [[action-identify-job-to-be-done|identify and articulate the job to be done]] and back it with [[action-provide-proof-of-expertise|structured proof of expertise]]. [[entity-the-ordinary|The Ordinary]] and [[entity-nike-d10|Nike]] exemplify resolution-friendly content; [[entity-lincoln|Lincoln]]'s 'elegance' positioning is the anti-pattern.

**Enrichment:** The label 'resolution optimization' is article-specific, but the behavior is well-supported by AI-search literature, which stresses 'high-information-gain content,' 'content depth and completeness,' and 'authority-first content' as drivers of AI citation. RLHF-tuned models are trained to maximize perceived helpfulness and correctness, which aligns closely with resolution. **Caveat:** some AI products still incorporate engagement signals, user feedback (thumbs up/down), and personalization, so 'resolution-only' is an oversimplification — accurate as the *dominant* design goal of answer engines, not the sole one (see [[contrarian-aspirational-marketing-is-a-liability]]).