Large language models (LLMs) require context to produce accurate responses, which is where Retrieval-Augmented Generation (RAG) comes in. Observability in RAG systems involves monitoring metrics like Precision, Recall, MAP, MRR, and NDCG to ensure reliable performance. Choosing the right metrics and mitigating biases when using LLMs as judges are crucial for evaluating system effectiveness.
