What is the easiest way to start monitoring AI model drift in our customer service chatbot since answers seem to be getting worse over the last few months
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the easiest way to start monitoring AI model drift in our customer service chatbot since answers seem to be getting worse over the last few months
1 real search · 1 real-search pass
AI chatbot evaluation monitoring production drift answer quality evaluation dataset metrics official documentation OpenAI evals LangSmithGoogle ↗
OBSERVED
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.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
AI chatbot evaluation monitoring production drift answer quality evaluation dataset metrics official documentation OpenAI evals LangSmith Google ↗
- LangSmith website · langchain.com
- Phoenix website · phoenix.arize.com
- Evaluation best practices · developers.openai.com
- Agent evaluation guide · developers.openai.com