Evaluating RAG Model Performance Metrics, Bias, and Interpretability
summary
A concrete RAG pipeline is built end to end: chunking text with tunable chunk size, creating embeddings, storing vectors in Chroma and Pinecone, retrieving top‑k (example k=5) and generating with LLaMA 3. Retrieval evaluation uses recall, precision (example 4/5 → 80%), and MRR; generation checks faithfulness and hallucination rates; system‑level checks measure latency. Bias detection and explainability workflows are sketched and open‑source tooling is recommended for automating metric collection and drift monitoring.
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chapters (10)
- 0:00RAG evaluation needs
- 1:11RAG pipelines need evaluation
- 2:36RAG architecture benefits
- 4:26LLM limitations addressed by RAG
- 6:12RAG evaluation metrics
- 7:19RAG pipeline creation
- 9:28Retriever and generator details
- 11:37Retrieval evaluation metrics
- 12:19Recall precision MRR
- 14:24Interpreting MMR evaluation
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