A structured framework for answering fine-tuning vs RAG interview questions, covering when to use each, cost comparison, latency, knowledge currency, and hybrid approaches.
A structured framework for answering Agentic RAG interview questions — tool-calling retrieval agents, Self-RAG, CRAG, and routing between retrieval strategies.
A structured framework for answering RAG embedding model interview questions — OpenAI vs Cohere vs BGE vs E5, dimensionality tradeoffs, MTEB benchmarks, and when fine-tuning actually pays off.
A structured framework for answering RAG evaluation interview questions, covering faithfulness, answer relevancy, context relevancy, RAGAS, and human eval correlation.
A structured framework for answering Graph RAG interview questions — knowledge graph construction, entity extraction, community detection, and Microsoft's GraphRAG approach.
A framework for answering hybrid search interview questions in RAG systems — BM25 + dense retrieval, reciprocal rank fusion, when sparse beats dense, and weighted scoring in practice.