Evaluate and secure a RAG system
Measure retrieval and answer quality while protecting sensitive knowledge.
- Name useful retrieval and generation metrics and identify core RAG security risks.
Evaluate retrieval separately from generation: did the right evidence appear, and did the answer use it correctly? Build a representative test set and track relevance, faithfulness, citation support, latency, and cost. Secure the full data path with access controls, careful ingestion, and defenses against prompt injection and sensitive-data exposure.
A small example
1retrieved_relevant = 2
2relevant_in_corpus = 3
3recall_at_k = retrieved_relevant / relevant_in_corpus
4print(f"Recall: {recall_at_k:.2f}")Recall: 0.67
A useful evaluation set includes answerable and unanswerable questions, varied wording, and realistic user permissions. Measure whether retrieval found the expected sources (such as recall@k), then inspect whether the final answer is supported. Never rely on the model alone to enforce authorization; filter data before providing it as context.
Key takeaways
Name useful retrieval and generation metrics and identify core RAG security risks.
Check that retrieved evidence is relevant, current, and allowed for this user.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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