RAG Architecture interview questions
Design retrieval-augmented generation systems end to end — chunking, embeddings, hybrid retrieval, reranking, grounding, and diagnosing whether a failure is retrieval or generation.
44 questions31 free to practice13 company-verified (Pro)
Free to practice
- Chunk Text on Sentence Boundaries
- 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 Token Cost Optimisation
- RAG System Retrieval Failure — Diagnosis and Fix
- Chunking Strategy Failure Analysis
- RAG system evaluation metrics and methodologies for production assessment.
- 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?
- How do you reduce token usage in a high-volume LLM application?
- What is your chunking strategy — by length, semantics, or structure?
- How do embeddings work in large language models?
- RAG retrieval accuracy: diagnosis, benchmarking, optimization, evaluation
- Text Chunking Strategies: Fixed-Size, Recursive, Semantic Methods
- Embedding Model Selection for RAG System Requirements
- Fixed versus semantic versus recursive text chunking methods
- Text Processing: Chunking Methods for NLP and Embeddings
- Optimal chunk size selection for retrieval augmented generation systems
- Production Monitoring — LLM Hallucination Detection System
- Fine-Tuning vs RAG — Choosing the Right Approach
- Your embedding model changes — how do you migrate 50M vectors safely?
- How do you evaluate retrieval quality in a RAG system?
- How do you update or backfill embeddings with zero downtime?
- How do you choose a vector database for production RAG?
- 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 RAG Architecture 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
- RAG Pipeline Missing Document Metadata During Chunk Ingestion
- Access control filter placement before reranker causes leakage bug
- Retrieval deduplication: chunks from same document not filtered before ranking.
- Hallucination Detection Pipeline Fails On Contradictory Claims Against Context
- LLM RAG efficiency metrics latency throughput token usage cost analysis
- Optimise a RAG pipeline that is 3x over token budget
- Implement a RAG pipeline with citation tracking
- Multi-tenant RAG: Secure Vector Index Isolation for Enterprise
- Indexer and retriever system design for efficient data lookup
- Enterprise RAG Pipeline: Scalable Search with Sub-200ms Latency Guarantees
- Insurance Claims Agent with RAG, Cost-Optimized LLM Integration
- Design a multi-tenant vector database for 10,000 enterprise customers
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