Faithfulness

A RAG pipeline just shipped for internal docs. How would the team know if retrieval is actually helping?

A colleague claims their model is 'grounded' because it cites sources. Explain why citation presence alone does not prove factual grounding.

Describe a concrete, production runnable mechanism to detect hallucinations in a RAG answer (claims unsupported by the retrieved chunks), as the answer is generated or just after.

Match each RAGAS metric to what it specifically measures about a RAG pipeline.

Your RAG system's faithfulness metric just dropped from 0.91 to 0.74 while context recall stayed flat at 0.85. What's the most likely pipeline layer to investigate first?

Complete the claim: the two-axis decomposition of RAG eval that diagnoses which layer failed.

Which statements correctly describe the four RAGAS metrics?

Match each RAGAS metric to the specific RAG quality problem it detects

What four metrics does the RAGAS paper propose and what does each measure?

Explain faithfulness vs answer relevance in RAG evaluation with concrete examples

Define faithfulness in RAG evaluation and distinguish it from answer relevance

What does context recall measure in RAG eval and what ground truth does it require?

What does context precision measure in RAG evaluation and what does low context precision indicate?

Predict what slice-level eval reveals about an 85% faithful RAG system suspected to fail on multi-hop queries

Describe two automated hallucination detection techniques and their tradeoffs

What is the main limitation of self-check consistency as a hallucination detection technique?

Compare Promptfoo, DeepEval, and LangSmith as LLM eval frameworks: when to use each?

Clarify the relationship between unfaithfulness and hallucination in RAG systems

Is 'unfaithful' the same as 'hallucinated'? What is the relationship between these concepts?

Explain FActScore's approach to long-form factual evaluation and its design decisions

What does FActScore measure and how does it decompose long-form factual evaluation?

Which of these are valid concerns when using LLM-as-judge for evaluation?

Which metric best measures whether a RAG answer is grounded in the retrieved context?