In this article:
1. Why LLM Tracking Matters
Large Language Models (LLMs) such as ChatGPT (OpenAI), Perplexity, Claude (Anthropic), and others are rapidly transforming how users search for information. Instead of browsing long lists of search engine results, many users now rely on AI-generated answers that:
Summarize results
Recommend products or services
Cite authoritative websites
Highlight specific brands or competitors
This creates a new SEO battlefield: AI visibility. Just like traditional search rankings, your brand’s visibility inside LLM-generated answers impacts customer acquisition, brand trust, and market perception.
Nightwatch’s LLM Tracking module is built to give you granular insights into how your brand (and competitors) are represented in AI answers — across providers, prompts, and geographies.
With LLM Tracking, you can:
Monitor brand mentions in AI answers
Compare against competitors and alternatives
Identify citation domains LLMs pull from
Track sentiment analysis (positive, neutral, negative framing)
Optimize for AI-driven discoverability
2. How to Use LLM Tracking
Step 1: Add Prompts
Prompts are the exact questions or instructions that LLMs respond to (e.g. “What is the cheapest money transfer service in the US?”). These prompts replicate real user intent queries.
Go to LLM Tracking → Prompts
Add your prompt(s)
Select provider (e.g., OpenAI, Perplexity)
Choose location & language (e.g., 🇺🇸 English, 🇩🇪 German)
Save and start tracking
Step 2: Analyze Results
Once prompts are added, Nightwatch automatically fetches and monitors answers, extracting entities, citations, positions, and sentiment.
3. Dashboard Views and Metrics
LLM Tracking includes four main dashboards: Overview, Prompts, Entities, and Citations.
📊 Overview Tab
The Overview gives you a bird’s-eye view of your AI visibility.
Metrics:
Average Position → Indicates the average order in which your entity appears in LLM answers. A lower number (e.g., #2) means your brand is mentioned early and prominently.
Visibility → Percentage of prompts where your entity appeared. 100% = always present.
Entities distribution → Pie chart showing the most frequently mentioned entities (brands, competitors, products).
Citations distribution → Pie chart showing the most frequently cited domains in AI responses.
Trend graph → Time-series data showing changes in your average position.
👉 How to use it:
Spot whether your visibility is increasing or decreasing.
Benchmark your position against major competitors.
Quickly identify which competitors or alternative products are dominating AI results.
📝 Prompts Tab
This section lists every prompt you’re tracking.
Columns explained:
Prompt → The exact tracked question.
Provider → Which LLM gave the answer (OpenAI, Perplexity, etc.).
Model → Model or channel (UI/API).
Location → Country/language for localized testing.
Visibility → How often tracked entities appear in responses (e.g. 92.7%).
Count → Total number of answers collected and analyzed.
Entities → Number of unique entities extracted from responses.
Updated → Last refresh date.
👉 How to use it:
Compare visibility differences between OpenAI and Perplexity.
Test location-based variations (answers in US vs. UK vs. Germany).
Track both generic prompts (“best money transfer service”) and branded prompts (“Is Western Union safe?”).
🏷 Entities Tab
The Entities tab is central for competitor benchmarking. Nightwatch applies Named Entity Recognition (NER) to classify mentions.
Metrics:
Entity → Brand, product, company, or technology detected (e.g. Wise, Zelle, Western Union).
Type → Product, company, technology.
Visibility → Share of tracked prompts where the entity appears.
Share of Voice → Entity’s market share in LLM responses, compared to all others.
Average Position → Average ranking/position in answers.
Mentions → Total times the entity was cited across prompts.
Trend → Whether entity mentions are increasing or decreasing over time.
Sentiment → Scored on a positive–negative scale, showing how the LLM frames the entity.
👉 How to use it:
See whether your competitors are gaining ground.
Detect new entrants into your market (emerging entities).
Monitor brand reputation by sentiment score.
🌐 Citations Tab
This view shows the web domains cited by LLMs as references.
Metrics:
Domain → Website cited (e.g. nerdwallet.com, bankrate.com, investopedia.com).
Mentions → Number of times cited across prompts.
Avg Position → Average placement in the answer. Lower = stronger authority.
Prompts → Number of unique prompts that cited this domain.
👉 How to use it:
Identify trusted sources LLMs prefer (these shape AI-generated answers).
Build a content/PR strategy to get coverage on these high-authority sites.
Track whether your competitors appear more often on top-cited domains.
4. Best Use Cases for LLM Tracking
Brand Monitoring → Ensure your company is consistently present in LLM answers.
Competitor Benchmarking → Compare your visibility and sentiment against direct competitors.
Reputation Management → Detect negative sentiment before it impacts perception.
Market Intelligence → Spot alternative providers, new products, or industry disruptors.
Citation Optimization → Identify the domains LLMs lean on most and strengthen your content presence there.
5. Example Prompts to Track
Comparison queries → “What is the best [product/service] in [location]?”
Price queries → “What is the cheapest [product/service]?”
How-to queries → “How should I [task]?”
Top lists → “What are the top 5 [products/services]?”
Brand vs competitor → “Is [brand] better than [competitor]?”
👉 Mix generic queries (to track industry-wide presence) and branded queries (to measure brand-specific performance).
6. Tips for Maximizing Value
Track multiple providers — ChatGPT, Perplexity, Claude answers often differ.
Use the Entities tab to uncover hidden competitors or alternatives you hadn’t considered.
Regularly review the Citations tab to build partnerships and backlinks with trusted domains.
Utilize sentiment metrics to identify early risks or opportunities for reputation management.
Align LLM insights with SEO strategy: improving your presence on citation domains often boosts both search rankings and AI visibility.
7. Example Analysis
Prompt: “What is the cheapest money transfer service in the US?”
Entities detected: Zelle, Wise, Venmo, Western Union
Visibility: Western Union appears in 78% of responses, Wise in 68%
Sentiment: Wise = 74 (positive), Western Union = 64 (neutral-positive)
Citations: Nerdwallet.com and Bankrate.com most frequently cited
Actionable insight:
Western Union is present but lags Wise in positioning.
Content presence on Nerdwallet and Bankrate is critical for improving LLM coverage.
Nightwatch’s LLM Tracking provides the first SEO-native framework to analyze AI-driven visibility. By tracking prompts, entities, sentiment, and citations across providers, you can:
Benchmark brand performance inside LLMs
Detect shifts in competitive landscape
Influence AI-generated recommendations through smart SEO and content strategies
👉 As LLMs continue to shape how users discover services, tracking and optimizing your AI visibility is as important as traditional SEO.