LLM Evaluation interview questions
Prove an LLM system works: eval design, LLM-as-a-judge pipelines, faithfulness and relevance metrics, regression tests, and measuring quality on real production data.
38 questions26 free to practice12 company-verified (Pro)
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- Vector Database Architecture — Conceptual Questions
- Embedding Similarity — Semantic Reasoning
- Token and Context Window Capacity Planning
- Parent-child chunking improves hierarchical document retrieval accuracy
- Top-K Retrieval by Cosine Similarity
- RAG System Evaluation — Diagnosing Low RAGAS Scores
- RAG System Retrieval Failure — Diagnosis and Fix
- Chunking Strategy Failure Analysis
- Detect and mitigate AI model hallucinations in production systems
- RAG system evaluation metrics and methodologies for production assessment.
- Detect hallucinations early, implement verification layers, provide user feedback mechanisms.
- RAG System Evaluation: Faithfulness, Relevance, Context Precision
- Optimal chunking strategy balancing context preservation and retrieval precision
- What is the right database for this AI task — SQL, NoSQL, or vector?
- When should you quantize a model and what are the tradeoffs?
- What is your chunking strategy — by length, semantics, or structure?
- RAG retrieval accuracy: diagnosis, benchmarking, optimization, evaluation
- Text Chunking Strategies: Fixed-Size, Recursive, Semantic Methods
- Optimal chunk size selection for retrieval augmented generation systems
- Production Monitoring — LLM Hallucination Detection System
- Your embedding model changes — how do you migrate 50M vectors safely?
- What fallback strategy do you use when an LLM fails mid-task?
- How do you evaluate retrieval quality in a RAG system?
- How do you update or backfill embeddings with zero downtime?
- How do you build and maintain memory in LLM applications?
- How do you track, version, and backfill changing context in LLM applications?
Company-verified Pro
Real LLM Evaluation questions reported from interviews at 50+ AI companies, with the golden answer and full production scoring. Unlock with Pro →
- RAG Chatbot Ignoring Retrieved Documents, Using Training Data Instead
- Evaluation Metric Misalignment: Faithfulness Scores Mask Semantic Errors
- Retrieval deduplication: chunks from same document not filtered before ranking.
- Hallucination Detection Pipeline Fails On Contradictory Claims Against Context
- Building Customer Support Agent Evaluation Harness: Metrics Design and Contamination Prevention
- ChatGPT Product Strategy and Metrics Design
- LLM performance evaluation metrics accuracy precision recall F1 score
- LLM RAG efficiency metrics latency throughput token usage cost analysis
- Mitigating Hallucinations In Production Generative AI Systems
- Implement a RAG pipeline with citation tracking
- LoRA vs QLoRA vs Full Fine-Tuning for Mistral-7B Legal Domain Adaptation
- Measuring and Mitigating Hallucination in Production LLMs at Scale
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