RAG interview question · Most asked
RAG leaf: How do you evaluate faithfulness of answers?
Summary
NLI/LLM-as-judge against retrieved context, citation coverage checks, and human spot audits.
Longer Answer
NLI/LLM-as-judge against retrieved context, citation coverage checks, and human spot audits. Fail closed when unsupported.
Key Points
- NLI/LLM-as-judge against retrieved context, citation coverage checks, and human spot audits
- Fail closed when unsupported
- Groundedness evaluation
- Use the answer to connect the concept back to a realistic production or interview scenario for 'RAG leaf: How do you evaluate faithfulness of answers?'
Comparison / Tradeoffs
| Aspect | Notes |
|---|---|
| Core idea | NLI/LLM-as-judge against retrieved context, citation coverage checks, and human spot audits. |
| Practical tradeoff | Groundedness evaluation. |
Explanation
Summary
Groundedness evaluation.
Longer Answer
Groundedness evaluation. Focus on the practical implications across Ai, Rag, Evaluation.
Key Points
- Groundedness evaluation
- Focus on the practical implications across Ai, Rag, Evaluation
- Relevant areas: Ai, Rag, Evaluation
- Use the answer to connect the concept back to a realistic production or interview scenario for 'RAG leaf: How do you evaluate faithfulness of answers?'
Comparison / Tradeoffs
| Aspect | Notes |
|---|---|
| Core idea | Groundedness evaluation. |
| Practical tradeoff | In an interview, relate 'RAG leaf: How do you evaluate faithfulness of answers?' to implementation decisions, tradeoffs, and operational impact. |
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