how do i know if my ai models are drifting over time and what is the simplest way to monitor that
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
how do i know if my ai models are drifting over time and what is the simplest way to monitor that
3 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
Evidently AI monitoring data drift documentation open source 2026 Google ↗
Arize Phoenix LLM observability evaluation tracing documentation Google ↗
Langfuse LLM observability evaluations monitoring documentation Google ↗
- GitHub - evidentlyai/evidently: Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics. · github.com
- GitHub - Arize-ai/phoenix: AI Observability & Evaluation · github.com
- LLM Observability & Application Tracing (Open Source) · langfuse.com
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Evidently AI monitoring data drift documentation open source 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Short fact | — | — | 1x of 1 | |
Arize Phoenix LLM observability evaluation tracing documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
Langfuse LLM observability evaluations monitoring documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |