This is a beast of a question and I underestimated how many dimensions they actually wanted covered.
Start by clarifying requirements and scale, then propose a hybrid architecture combining a fast in-memory prefix index (e.g., trie or finite-state transducer) for base suggestions with a lightweight ML re-ranker for personalization and trending. Address each requirement (latency, typo tolerance, multilingual, privacy) with specific techniques and trade-offs, and discuss how to measure success with online metrics.
Pro tip: Emphasize that sub-100ms latency requires precomputation and caching at the edge, and that personalization must be done with a small, fast model to avoid adding latency. Also, mention that privacy safeguards like differential privacy and on-device processing can be differentiators.
Ask about expected QPS, latency SLA, data volume, languages, and privacy constraints. Define success metrics like suggestion acceptance rate and time-to-first-keystroke.
Propose a fast prefix-based retrieval using a trie or FST with precomputed top-K suggestions per prefix. For typo tolerance, incorporate edit-distance or neural embeddings with approximate nearest neighbor search.
Use a lightweight ranking model (e.g., gradient boosted trees or small neural net) that combines user history, query popularity, and recency. For trending, maintain a real-time stream processing pipeline to update scores frequently.
Support multiple languages via language-specific tokenization and models, or a multilingual model. Implement abuse detection with anomaly detection and rate limiting, and privacy via differential privacy, data anonymization, and user opt-outs.
Compare latency vs. accuracy, personalization vs. privacy, and precomputation vs. dynamic updates. Propose A/B testing and offline metrics like MRR and recall@K.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.