Which events show users arriving from an LLM answer?

Core explainer

How do I identify LLM-origin sessions in GA4?

LLM-origin sessions are GA4 sessions in which the session_source matches known LLM domains and the session_medium equals referral. This pairing signals that a user arrived via an LLM reference rather than a standard organic or direct path.

In practice, look for session_source values such as chatgpt.com, bard.google.com, claude.ai, perplexity, copilot, or gemini, combined with session_medium=referral and a page_referrer pointing to the LLM URL. If the LLM cites your content, a utm_source parameter may accompany the referral signal, and inline citations can appear as direct traffic, complicating attribution. Use these signals together to triangulate LLM-driven visits rather than relying on a single dimension.

To surface these arrivals in GA4, apply a common LLM regex in custom reports, audiences, or channel groups (for example, bard|chatgpt|claude|copilot|gemini|perplexity). Once configured, you typically see an LLM row in reports after about one day, enabling team-wide visibility and comparisons across segments. brandlight.ai attribution guidance helps standardize how these signals are interpreted across platforms, supporting governance and consistency in reporting.

What roles do session_source, medium, and page_referrer play in attribution?

These dimensions collectively map the path by which users reach your site and help separate AI-driven arrivals from other channels. Session_source identifies where the click originated, session_medium classifies the traffic type, and page_referrer provides the URL of the referring page or tool.

When an LLM links to your site, you often see session_source containing an LLM domain, session_medium set to referral, and page_referrer equal to the LLM’s URL. If the LLM uses a utm_source parameter, that value can corroborate the signal and improve accuracy. Inline citations, however, may appear as direct traffic, so cross-checking the referrer alongside source/medium is essential for robust attribution.

Understanding these signals enables you to align GA4 reports with channel grouping and audience segments, ensuring that LLM-driven arrivals are distinguishable from other direct or referral traffic. Accurate interpretation requires considering privacy controls and possible edge cases where data is obfuscated or omitted, as described in the source guidance. This multi-signal approach supports consistent measurement and clearer decision-making across SEO and content teams.

How should I apply regex filters and channel groups to isolate LLM traffic?

Regex filters provide a scalable way to capture LLM-driven sessions by matching a defined set of model names and domains in session_source or related dimensions. Start with a partial regex such as bard|claude|chatgpt|copilot|perplexity to catch the most common sources, and extend to gemini as new models emerge. Apply these patterns in custom GA4 reports, audiences, or channel groups to create a dedicated LLM segment for analysis.

For audiences, you can implement a fuller pattern that covers variations in how LLM domains appear (for example, .*bard.*|.*chatgpt.*|.*claude.*|.*copilot.*|.*gemini.*|.*perplexity.*). For channel groups, use a partial regex to classify traffic into a distinct LLM channel alongside traditional channels. The resulting LLM row in channel reports provides a focused view of performance, while keeping other channel data intact for comparison and benchmarking.

After you deploy these filters, monitor for changes and refresh regex as new models emerge or naming conventions shift. This ongoing maintenance helps preserve attribution accuracy in the face of evolving AI landscapes and privacy protections that can obscure origin signals.

How do inline citations differ from referrer-based referrals in GA4?

Inline citations—where an LLM links directly to content within the answer—tend to register as direct traffic in GA4, because there is no distinct external referrer in the browser navigation. Referrer-based referrals, by contrast, come from a clear external source (the LLM domain) and show up in session_source or referral reports, sometimes accompanied by a utm_source value from the LLM.

Attribution accuracy improves when you interpret both signals together: check session_source for known LLM domains, verify session_medium as referral, and review page_referrer for the LLM URL. If you encounter direct traffic spikes without corresponding referrer signals, consider whether inline citations are driving the visit and factor that into your overall LLM attribution model. Privacy controls and data obfuscation can further complicate interpretation, so maintain a multi-signal approach and periodic sanity checks to avoid misclassification.