Intermediate~2h
RAG Evaluation in RAG & Retrieval SystemsInterview Readiness Check
What RAGAS measures and how, faithfulness vs relevance vs groundedness, building a golden retrieval dataset, LLM-as-judge pitfalls specific to RAG, online vs offline evaluation..
RAGASfaithfulnessanswer relevancecontext recallLLM-as-judgegolden datasetoffline evaluationRAG metrics
Learning path
RAG Prompt Design→RAG Evaluation ← you are here→RAG Failure ModesRAG Fine-Tuning: RAFT, RA-DIT & Distillation
Try a sample question
Your RAG system retrieves a chunk stating 'The Eiffel Tower is 330 meters tall.' The generated answer says 'The Eiffel Tower is 330 meters tall and was built in 1887.' The build year does not appear in any retrieved context. How does RAGAS score this answer on the faithfulness metric?
A.Faithfulness is perfect because the 330-meter claim is correctly attributed and the 1887 date is a well-known fact
B.Faithfulness is penalized because the claim about 1887 is not grounded in the retrieved context, even if it happens to be factually correct
C.Faithfulness only measures whether the answer matches the user's question, not whether it is grounded in context
D.Faithfulness is not applicable here because the answer is factually accurate
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of rag evaluation fundamentals
- Trade-offs and design decisions in real system scenarios
- RAGAS and faithfulness — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the RAG Evaluation check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the RAG Evaluation check test?
- The check covers what ragas measures and how, faithfulness vs relevance vs groundedness, building a golden retrieval dataset, llm-as-judge pitfalls specific to rag, online vs offline evaluation.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for RAG Evaluation?
- Key concepts include: RAGAS, faithfulness, answer relevance, context recall, LLM-as-judge, golden dataset, offline evaluation, RAG metrics. These frequently appear in rag & retrieval systems interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study RAG Evaluation?
- Most engineers spend around 2 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before RAG Evaluation?
- RAG Evaluation builds on RAG Prompt Design. Complete those first for the best results.
- Is RAGAS, faithfulness, answer relevance covered?
- Yes. The RAG Evaluation module covers RAGAS, faithfulness, answer relevance among other key concepts. The check includes questions that reflect real rag & retrieval systems interview patterns.