Intermediate LevelAI, LLMs, Embeddings & MCP
How do automated RAG Evaluation frameworks (Ragas, DeepEval) measure Faithfulness and Answer Relevance?
❓ Interview Prompt: Explain LLM-as-a-Judge evaluation methodology, RAG triad metrics (Faithfulness, Answer Relevance, Context Recall).
Evaluating AI applications using manual human review does not scale. Automated evaluation frameworks (e.g. Ragas, DeepEval) use LLM-as-a-Judge methodology to compute standard metrics:
The RAG Evaluation Triad Metrics:
1
Faithfulness: Measures whether claims in the generated answer are strictly derived from retrieved context chunks (detects hallucinations).
2
Answer Relevance: Measures whether the generated output directly answers the user query.
3
Context Recall / Precision: Measures whether the vector database retrieved all ground-truth context facts without noisy, irrelevant chunks.
Code Implementation Example
typescript
// DeepEval evaluation test case definition concept
interface RAGTestCase {
input: string;
actualOutput: string;
retrievedContext: string[];
}
async function evaluateFaithfulness(testCase: RAGTestCase): Promise<number> {
const evalPrompt = "Given Context: " + testCase.retrievedContext.join(' ') + "\n" +
"Statement: " + testCase.actualOutput + "\n" +
"Rate Faithfulness from 0.0 to 1.0 (Is the statement 100% supported by context?). Output JSON.";
const res = await judgeLlm.generate(evalPrompt);
return JSON.parse(res).score;
}💡Automated Faithfulness metric evaluation using LLM-as-a-Judge.
🎯 Interview Key Takeaway
Automated RAG evaluation uses LLM-as-a-Judge frameworks to quantify Faithfulness, Answer Relevance, and Context Recall.
#LLM Eval#Ragas#DeepEval#Metrics#Intermediate
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