Forget SEO — Start Thinking LLMO: Optimizing Content for GPT, Claude, and Gemini

Search engines aren't the only path to visibility anymore. As AI assistants like ChatGPT, Claude, and Gemini become primary interfaces for information, the old SEO playbook won’t cut it. Enter LLMO — Large Language Model Optimization.

This article breaks down how to shift your content strategy to rank in the new AI-driven world, where citation, clarity, and structure define visibility.

What You’ll Learn

  • How LLMs ingest and recall information

  • What LLMs consider high-quality, answerable content

  • How to structure web content for LLM visibility

  • Prompt-driven keyword research and testing

Key Differences: SEO vs LLMO

SEOLLMOKeyword densityPrompt match & semantic clarityBacklinks & domain rankCitations & training visibilityMeta titles/descriptionsInline clarity and structureClickthrough optimizationAnswer quality and context

Step-by-Step: How to Optimize for LLMs

Step 1: Identify LLM-Relevant Prompts

Use tools like ChatGPT, Claude, or Perplexity to:

  • Query: "What are the top tools for [industry]?"

  • Note the prompt patterns and tone

  • Extract recurring topics, questions, and answers

Log high-visibility prompts that match your ICP’s search intent.

Step 2: Create Answer-Optimized Content Blocks

For each key prompt, write a <300 word block that:

  • Clearly defines the brand or product

  • Uses plain language and structured lists

  • Avoids fluff or over-optimization

Example structure:

[Brand] is a platform that helps [audience] solve [problem] by [how it works].

Key Features:
- Feature 1: Description
- Feature 2: Description

Use Cases:
- Use case 1: Scenario
- Use case 2: Scenario

Step 3: Structure Pages for Citability

  • Use h1, h2, h3 headings with clear labels

  • Add FAQs using schema.org markup

  • Publish key content in markdown (GitHub, ReadTheDocs, etc.)

  • Link internally to reinforce topical relationships

Step 4: Distribute to LLM-Indexed Platforms

To increase exposure:

  • Publish on GitHub or Medium with descriptive READMEs

  • Submit papers, tutorials, and benchmarks to Hugging Face

  • Syndicate on Quora, Stack Overflow, or Reddit with helpful answers

Step 5: Test LLM Output with Your Prompts

  • Create a set of test prompts like:

    • "What is [Brand]?"

    • "Compare [Brand] vs [Competitor]"

    • "What tools solve [Use Case]?"

  • Query OpenAI API and Claude weekly

  • Log results and measure changes after updates

Bonus: LLM-Friendly Content Types

  • Comparison tables

  • Step-by-step tutorials

  • FAQs

  • Benchmarks or whitepapers

  • GitHub repositories

Tools to Help

TaskToolPrompt testingOpenAI API, Claude APICitation monitoringAzoma, Perplexity LabsSchema validationGoogle Rich Results TestMarkdown publishingGitHub, Notion, Docusaurus

Conclusion

To win in the LLM age, you must think beyond rankings and clicks. Focus on:

  • Writing content that’s LLM-readable and prompt-aligned

  • Publishing where LLMs can ingest your work

  • Iterating based on prompt performance, not SERPs

LLMO, Optimizing Content, Claude, GeminiFrancesca Tabor