RAG Evaluation: Measure Retrieval Recall & Faithfulness

Description

RAG evaluation: measure retrieval recall and answer faithfulness to pinpoint whether failures come from retrieval or generation. Learn a practical pipeline to compute recall@k and faithfulness on small test sets using token-overlap retrievers, then scale to vector/ANN indexes in Python for targeted debugging. Apply stepwise baselines—top-k retrieval, recall@2/3, faithfulness overlap, and a combined metric—to know exactly where to fix your RAG system. Subscribe for concise AI engineering and LLM system tutorials. #RAG #RetrievalAugmentedGeneration #AIEngineering #NLP #LLMs #MachineLearning #Python