Monitor your LLMO performance like a pro

This post is part of our ongoing series on LLM optimization and content discovery. You can see previous stories here:

• Beyond SEO: How to optimize your content for AI discovery
• Structuring content for LLM discoverability
• Making your content AI-trusted and citable
• Search intent: The key to AI optimization

Picture it: Your meticulously optimized blog post ranks #1 on Google. But when 24% of your target audience turns to ChatGPT before Google and 77% use it as a search engine altogether, a large number of readers may never see your content if you rely on traditional search results alone. You’re stuck in the LLMO visibility gap, and the only way out is to monitor AI search performance.

Monitor the right platforms

Traditional SEO monitoring tools won't tell you whether AI systems are using your content. You need to actively test across the platforms where your audience is actually getting answers. For example, you could use tools such as Perplexity, Google Search Generative Experience, or ChatGPT to regularly query topics related to your content. The key is to test queries regularly across multiple platforms. What works on one may not surface on another, and each system has different citation behaviors and content preferences.

Implement prompt testing as your new audit method

Think of prompt testing as your LLMO equivalent of keyword research. Instead of checking where you rank for "content marketing tips," you need to understand whether AI systems cite your work when users ask, "How do I improve my content marketing strategy?"

Start with natural language queries that match how your audience actually asks questions. Test variations:

• Direct questions: "What is LLMO?"
• Problem-focused queries: "Why isn't my content showing up in ChatGPT responses?"
• Comparison requests: "What's the difference between SEO and LLMO?"
• How-to questions: "How do I optimize content for AI search?"

Make sure you document what you find. Create a simple spreadsheet tracking:

• The query you tested
• Which platform(s) you tested on
• Whether your content appeared
• If it appeared, how it was cited or referenced
• What competing sources were cited instead

This audit reveals not just whether you're visible, but why certain content succeeds while other pieces don't. You may notice patterns. Perhaps your long-form guides get cited more than brief posts, or your data-backed articles outperform opinion pieces.

Track your AI visibility over time

Unlike traditional search rankings that you can monitor daily through tools like Google Search Console, AI visibility requires more manual detective work. Here are strategies to establish a tracking baseline:

• Create a prompt testing schedule. Consistency matters more than frequency. Test the same set of core queries every quarter across the same platforms to identify trends.
• Survey your audience. Ask questions such as “Which tools did you use to find my content (e.g., Google, ChatGPT, Perplexity)?” You can add this question to newsletter signups, surveys, or contact forms. The data you gather here can reveal if AI tools are driving discovery.

The emerging ecosystem of LLMO monitoring tools is still immature compared to SEO platforms, but staying manually engaged with these platforms helps you understand the landscape as it evolves.

The quarterly LLMO content audit

Every quarter or so, take time for a comprehensive LLMO content audit to evaluate whether your content meets the needs of AI systems.

• Review your top-performing content from traditional search and social channels. Run representative queries through AI platforms to see if this high-value content also performs well in LLMO contexts.
• Identify content gaps. If your competitors' content consistently appears in AI responses while yours doesn't, analyze what might make their content more citation-worthy. Is it structure? Depth? Authority signals? Source quality?
• Audit for answer-worthiness. AI systems prioritize content that directly answers questions. Review your content library and ask: Does this piece clearly answer a specific question? Is the answer easy to extract? Would I cite this source if I were compiling an answer?
• Update underperforming content with LLMO principles in mind: clear structure, authoritative sources, concise answers followed by depth, and well-defined expertise.
• Test, measure, test again. After updating content, give it a few weeks, then run your prompt tests again to see if visibility improves.

Your next steps: Revisit your content library through an LLMO lens

Every day that your content remains optimized solely for traditional search engines is a day you're potentially missing audiences who are getting their answers from AI systems. Here's your challenge: Choose five of your most important content pieces. Run prompt tests on them across Perplexity, Google AI Overviews, and Bing Chat this week. Document what you find. Are they showing up? Are competitors' sources being cited instead? What patterns emerge?

This simple exercise will reveal more about your LLMO visibility than months of theorizing. Those results will help you decide your next steps. 

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