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LLM Monitoring
Definition: Tracking how large language models mention, describe, and recommend your brand across AI platforms.
LLM Monitoring is the practice of tracking how large language models (LLMs) like GPT-4, Claude, Gemini, and others mention and describe your brand.
The LLM Landscape
Major LLMs to monitor include:
Conversational AI
- ChatGPT (OpenAI)
- Claude (Anthropic)
- Gemini (Google)
- Grok (xAI)
AI Search
- Perplexity
- SearchGPT
- Bing Copilot
Specialized
- Industry vertical AI tools
- Enterprise AI platforms
Why Monitor Multiple LLMs?
Each LLM has:
- Different training data
- Different knowledge cutoffs
- Different citation behaviors
- Different user bases
Monitoring only one platform provides an incomplete picture.
What to Monitor
- Mention frequency - How often are you mentioned?
- Mention context - In what situations does AI mention you?
- Accuracy - Is the information correct?
- Sentiment - How does AI describe you?
- Competitive position - Who else is mentioned?
Building an LLM Monitoring Program
- Define key queries - What prompts matter for your brand?
- Test systematically - Query each platform regularly
- Track trends - Monitor changes over time
- Act on insights - Use data to improve visibility
LLM monitoring is essential for any brand serious about AI visibility. Platforms like Prompt Eden automate this across 22+ LLM providers.