How to Track Whether LLMs Recommend Your Brand
· 10 min read · Guide
You Can't Improve What You Don't Measure
Most brands have no idea how AI models talk about them. They optimize for Google rankings but ignore the growing channel where buyers ask ChatGPT, Gemini, Claude, and Perplexity for recommendations. Tracking your LLM recommend status is the first step to owning your category in AI.
What Is an LLM Recommendation Audit?
An LLM recommendation audit systematically tests how major AI models respond to queries relevant to your brand and category. Instead of guessing, you ask each model the exact questions your buyers ask — and record whether your brand appears, where it ranks, and what sentiment surrounds it.
The 5-Step Tracking Framework
Step 1: Define Your Target Prompts
Start with 15–30 prompts that mirror real buyer queries like "What's the best [category] tool?" and "Compare [brand] vs [competitor]."
Step 2: Query All Major LLMs
Test across ChatGPT, Claude, Gemini, Perplexity, Grok, and DeepSeek. Each model has different training data and weighting.
Step 3: Score Each Response
Record whether you're mentioned, your position, sentiment (positive/neutral/negative), and the context of the recommendation.
Step 4: Calculate Your LLM Recommend Score
Track mention rate, top position rate, sentiment score, and coverage score across all models.
Step 5: Benchmark Against Competitors
Run the same prompts for competitors. The gap reveals where to focus your efforts.
How Often Should You Track?
Weekly for active campaigns, bi-weekly for monitoring, monthly at minimum to catch signal decay.
Common Patterns You'll Discover
Model-specific blindspots, competitor signal drift, sentiment disconnects, and category misattribution are the four patterns brands discover most often.
What To Do With Your Results
Fix blindspots first, address negative sentiment, strengthen top positions, and close the competitor gap.