Core

Evaluating RAG Systems

The central rule of RAG evaluation is to score retrieval and generation separately, because they fail for different reasons and you cannot fix what you cannot isolate. Retrieval is graded against a golden set with recall@k, precision@k, MRR, and NDCG; generation is graded for faithfulness and answer relevance, usually with an LLM judge. Recall@k is the ceiling on everything downstream.

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RELATED CONCEPTS

Retrieval-Augmented Generation (RAG)Golden Datasets and Eval Sets LLM-as-a-Judge Precision, Recall and F1

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks→RAG & Agent System DesignHow would you chunk documents for retrieval, and how do you know your chunking is good?→ML System Design (Product)Design an evaluation framework for an ads-ranking system.→RAG & Agent System DesignHow would you evaluate a RAG system before letting a customer launch it?→RAG & Agent System DesignHow would you chunk contracts, Slack threads, and PDFs full of tables: same pipeline or different?→Machine Learning & Data ScienceYour model scored 95% in the pilot and 70% in production. What happened?→