Rag

Scenario-based AI engineering interview preparation covering RAG, evaluation, reliability, and production trade-offs.

Diagnose AI application failures caused by oversized, incomplete, or poorly prioritized model context.

Diagnose retrieval quality problems caused by inconsistent embedding models, dimensions, preprocessing, or indexing pipelines.

Production GenAI architecture interview scenarios covering RAG, agents, security, cost, evaluation, and reliability.

A practical workflow for diagnosing weak semantic retrieval in AI applications.

A requirements-first architecture pattern for designing a production retrieval-augmented generation platform.

Diagnose unsupported RAG answers by separating retrieval, context construction, model behavior, and evaluation failures.