LLMrefs takes a different approach to AI visibility tracking. Instead of asking you to guess which prompts to track, they use a 4.5 million conversation dataset to generate prompts based on what people actually ask AI. It's keyword-first rather than prompt-first.
If you want data-driven prompt discovery, here's what to know.
How LLMrefs works
LLMrefs flips the typical workflow. You enter keywords (not prompts), and the platform automatically generates relevant prompts based on their 4.5M ChatGPT conversation dataset. You track what people actually ask, not what you think they ask.
The platform covers 11 AI engines: ChatGPT (including GPT-5), Google AI Overviews, AI Mode, Gemini, Perplexity, Claude, Grok, Copilot, Meta AI, and DeepSeek. That's one of the widest coverages available.
Keyword-based Tracking lets you enter keywords, and LLMrefs generates "fan-out prompts" based on real conversations. If you track "email marketing," you get visibility on dozens of actual prompts people use.
The ChatGPT Prompts Database is free to access. 4.5M real conversations from ChatGPT, Gemini, Perplexity, and other AI chatbots. You can explore what people ask before setting up tracking.
Geo-targeting covers 20+ countries and 10+ languages. Citation and Sentiment Analysis shows not just if you appear, but which sources are cited and how you're described.
LLMrefs pricing
LLMrefs offers a free tier plus paid plans. Free gives you access to the ChatGPT prompts database (no tracking). Pro is $79/month for 50 keywords, 11 AI engines, and 500 prompts/month. Enterprise is custom pricing.
The free database access is genuinely free, no credit card required. For actual visibility tracking, the Pro plan at $79/month is competitive given the 11-engine coverage.
What LLMrefs does well
The 4.5M prompt dataset means you track queries people actually use. No guessing, no assumption-based prompt lists.
Eleven AI engines including GPT-5, Claude, Gemini, and DeepSeek is impressive. Few tools match this breadth.
Enter keywords, get prompts automatically. Less manual prompt research, more automated discovery.
You can explore the ChatGPT prompts database without paying. Understand what people ask before committing to tracking.
$79/month for 11 AI engines is competitive. Similar coverage from other tools often costs more.
Where Mentionable takes a different approach
LLMrefs uses keyword-based automation with massive LLM coverage. Mentionable offers URL-based prompt suggestions with simpler tracking.
The workflows are different. LLMrefs: enter keywords, get auto-generated prompts from a dataset. Mentionable: enter your URL, we analyze your site and suggest purchase-intent prompts specific to what you offer.
50 keywords on Pro might feel restrictive for larger operations. Expanding requires enterprise pricing.
The automation means less direct control over exactly what gets tracked. If you want to specify exact prompts, Mentionable gives you more flexibility.
Which one fits you?
LLMrefs makes sense if:
- You want prompts based on real conversation data
- You need 11 LLM coverage
- You prefer keyword-first over prompt-first workflow
- You value the free database for research
- You have budget for $79/month
Mentionable might be better if:
- You want free tracking, not just free research
- 5 LLMs is enough coverage
- You prefer URL-based prompt suggestions
- You want straightforward prompt tracking
- Simplicity matters more than dataset depth
The bottom line
LLMrefs offers a genuinely different approach. The 4.5M conversation dataset means you track real queries, not guesses. Eleven LLM coverage is impressive. The keyword-first workflow reduces manual prompt research.
But it's not for everyone. The automation means less direct control. 50 keywords on Pro may limit larger operations. And if your niche isn't well-represented in their dataset, the value diminishes.
Try their free database first. Explore what people actually ask in your space. If the data resonates and you need broad LLM coverage, the $79/month Pro plan is reasonable. Otherwise, simpler tools like Mentionable might fit better.