A structured framework for answering vector database comparison questions in AI engineering interviews, covering Pinecone, Weaviate, Qdrant, Chroma, Milvus, managed vs. self-hosted tradeoffs, and cost analysis.
Production-tested vector database cost optimization tactics for 2026, from quantization to tiered storage.
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.