Real ChatGPT answer · index seedjlV3s4Ox9gocompleteB2B software · AI and automation toolsinformational
How can we set up automated evaluation pipelines using DeepEval or RAGas to continuously measure context recall and faithfulness in our enterprise AI knowledge base?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can we set up automated evaluation pipelines using DeepEval or RAGas to continuously measure context recall and faithfulness in our enterprise AI knowledge base?
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.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
DeepEval documentation RAG metrics contextual recall faithfulness evaluation dataset CI/CD Google ↗
Ragas documentation context recall faithfulness metrics evaluate RAG pipeline Google ↗
- Contextual Recall · deepeval.com
- RAG Evaluation · deepeval.com
- RAG Evaluation Quickstart · deepeval.com
- List of available metrics · docs.ragas.io
- Evaluate and Improve a RAG App · docs.ragas.io
- Faithfulness · deepeval.com
- RAGAS documentation · docs.ragas.io
- Context Recall · docs.ragas.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
DeepEval documentation RAG metrics contextual recall faithfulness evaluation dataset CI/CD OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
Ragas documentation context recall faithfulness metrics evaluate RAG pipeline OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |