Oracle ML Interview: RAG Systems & Retrieval Models

This prompt tests your practical knowledge of Retrieval-Augmented Generation (RAG) systems and your ability to apply research to production ML problems.

You will be expected to explain the core contributions of the RAG paper (how retrieval and generation are integrated, common retriever architectures like DPR, and generator decoders such as BART/transformers), describe retrieval mechanics (dense vs. sparse, ANN indexes), and contrast fusion strategies (retrieve-then-generate, Fusion-in-Decoder, token-level vs. sequence-level fusion). You should be able to walk through concrete implementation decisions: indexing pipelines, negative sampling for DPR, training regimes (separate vs. joint training), and latency/throughput trade-offs for serving.

Interview flow typically starts with a high-level explanation of RAG, moves to implementation details (retriever architecture, encoder/decoder choices, batching and ANN configuration), and finishes with evaluation and experiments. Be prepared to propose metrics (precision/recall of retrieved docs, F1/Exact Match for QA, ROUGE/BLEU for generation, and factuality/hallucination checks), design ablation studies and control groups (baseline RAG vs. RAG + reranker), and choose statistical tests to validate improvements.

Skill signals the interviewer looks for: understanding of retrieval models and index design, transformer-based generation tuning, experiment design and metrics, engineering trade-offs for scale, and strategies to reduce hallucination or noisy-context effects. Use concrete examples, justify trade-offs, and outline reproducible evaluation steps.