RAG evaluation
While traditional LLM evaluation focuses on the model’s inherent capabilities, RAG evaluation requires a more comprehensive approach that considers both the model’s generative abilities and its interaction with external information sources.
RAG systems combine the strengths of LLMs with information retrieval mechanisms, allowing them to generate responses that are not only coherent and contextually appropriate but also grounded in up-to-date, externally sourced information. This makes RAG particularly valuable in fields where current and accurate information is crucial, such as news reporting, research, and customer support.
The evaluation of RAG systems goes beyond assessing a standalone LLM. It requires examining the entire system’s performance, including:
- Retrieval accuracy: How well does the system fetch relevant information?
- Integration quality: How effectively is the retrieved information incorporated into the generated...
