Retrieval, rerankers, and RAG tips and tricks | Data Brew | Episode 39
Data Brew by Databricks
Evaluating Performance in RAG Teams
This chapter explores the philosophical underpinnings of evaluations within the RAG team at Databricks, focusing on the challenges of ensuring factual accuracy and the relevance of evaluation benchmarks. It discusses the tension between creativity and correctness in machine learning, along with innovative methods like using language models to improve evaluation processes.
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