Start by clarifying requirements and constraints (latency SLA, data freshness, multi-tenancy, PII) to frame the design. Then walk through the end-to-end system architecture, emphasizing the indexing pipeline, hybrid retrieval, ranking, and ACL enforcement. Finally, discuss evaluation, monitoring, and trade-offs, showing how each component meets the requirements.
Pro tip: Emphasize that ACLs must be enforced at query time with document-level filters applied before scoring to prevent leakage, and discuss how to handle PII with encryption and tokenization. Also, highlight the importance of offline/online evaluation and A/B testing for continuous improvement.
Ask questions to understand latency SLA (e.g., p95 < 200ms), data freshness (e.g., near real-time indexing), multi-tenant isolation, PII handling policies, and scale (documents, queries per second).
Outline the ingestion pipeline: document parsing, PII detection/redaction, chunking, embedding generation, and indexing into a hybrid store (e.g., vector DB + inverted index). Ensure multi-tenant separation via namespaces or metadata filtering.
Explain hybrid retrieval combining lexical (BM25) and semantic (dense vectors) search, followed by a learning-to-rank model. Include query understanding (intent classification, entity recognition) and personalization (user history, role-based boosts).
Describe how to enforce access control at query time: apply tenant and user ACL filters as pre-filters in the retrieval stage, ensuring no unauthorized documents are scored or returned. For PII, use encryption, tokenization, and redaction during indexing and retrieval.
Define offline metrics (NDCG, MRR) and online metrics (CTR, dwell time). Set up monitoring for latency, freshness, and leakage. Use A/B testing to validate ranking and personalization changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.