Design an enterprise RAG assistant for internal docs

## Scenario Design an **enterprise GPT-style assistant** that allows employees to ask questions about **internal company documents** (policies, wikis, specs, tickets, PDFs, etc.). The core approach is **Retrieval-Augmented Generation (RAG)**. The interviewer is primarily focused on **machine learning choices and training** rather than generic infrastructure. ## Requirements 1. Propose an end-to-end RAG system and explicitly break it into components: - **Retriever** (candidate generation) - **Evaluator** (reranker / verifier / filter) - **Generator** (LLM answering with citations) 2. For each component, discuss: - Model architecture choices (and why) - Training objective / loss functions - Optimizer and training recipe (batching, negatives, schedules, mixed precision, etc.) - Training data preparation (labeling strategies, weak supervision, synthetic data, privacy constraints) - Evaluation strategy (offline metrics + human eval + online/production monitoring) 3. Address common RAG failure modes (hallucination, stale content, conflicting docs, long documents) and how your modeling/training/evaluation handles them. Assume the system must respect **document-level permissions**, and responses should be **grounded** in retrieved sources with citations.