How do the evaluation metrics in Promptfoo differ from Braintrust when testing prompt regressions for our customer-facing conversational agents?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do the evaluation metrics in Promptfoo differ from Braintrust when testing prompt regressions for our customer-facing conversational agents?
2 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.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Promptfoo evaluation metrics assertions LLM eval metrics official docs Google ↗
Braintrust evaluation metrics scorers scoring functions official docs Google ↗
- Assertions and Metrics - LLM Output Validation · promptfoo.dev
- Model-graded metrics · promptfoo.dev
- AI agent evaluation platform | Evaluate AI agents · braintrust.dev
- RAG evaluation | RAG evaluation tools · braintrust.dev
- Assertions and Metrics - LLM Output Validation · promptfoo.dev
- How to analyze your eval results - Evals · braintrust.dev
- What is an LLM-as-a-judge? When to use it (and when to use deterministic evals) - Articles · braintrust.dev
- How to test AI models - Articles · braintrust.dev
Queries
2 queries · click a column to sort