Start by clarifying requirements: what data types, scale, latency, and accuracy needs. Then design the indexer (data ingestion, preprocessing, indexing algorithms) and retriever (query processing, ranking, serving), emphasizing trade-offs between recall, latency, and cost. Finally, discuss evaluation and iteration.
Pro tip: At Apple, privacy and on-device processing are critical. Mention how you'd handle sensitive data, possibly with federated learning or differential privacy, and how you'd optimize for Apple's hardware (e.g., Neural Engine).
Ask about data modality (text, images, etc.), scale (millions vs billions), latency constraints, and accuracy targets. Understand if it's for search, recommendations, or RAG.
Outline the indexer pipeline: data collection, preprocessing, feature extraction, embedding generation, and index building. Then the retriever: query understanding, candidate generation, ranking, and post-processing.
Discuss indexing algorithms (e.g., inverted index, HNSW, IVF-PQ), trade-offs between index size, build time, and query speed. Mention distributed indexing and incremental updates.
Explain retrieval strategies: lexical (BM25) vs semantic (embeddings), hybrid approaches, and re-ranking with cross-encoders. Cover serving infrastructure, caching, and latency optimization.
Define offline metrics (recall@k, MRR, NDCG) and online metrics (CTR, user engagement). Discuss A/B testing, feedback loops, and model retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.