I started with a trie-based approach for prefix matching and the interviewer seemed fine with it but kept nudging me toward personalization and context.
Start by clarifying requirements and constraints, then propose a hybrid approach combining a lightweight on-device n-gram model for speed with a cloud-based neural language model for accuracy. Discuss the trade-offs between latency, memory, and prediction quality, and outline how you would evaluate and improve the system.
Pro tip: Emphasize the importance of personalization and privacy: on-device learning from user's typing history can significantly improve predictions without sending sensitive data to the cloud. Also, mention the need for graceful degradation when network is unavailable.
Ask about latency requirements, memory limits, privacy concerns, and whether predictions should be personalized. Understand the scale (millions of users) and the need for offline functionality.
Propose a two-tier system: a fast, on-device n-gram or small neural model for immediate suggestions, and a more powerful cloud-based model (e.g., LSTM or Transformer) for refined predictions when connectivity allows.
Describe how to collect and anonymize typing data, train the model on large corpora, and continuously update it. Include techniques like federated learning to preserve privacy.
Discuss model compression (quantization, pruning), caching strategies, and efficient inference to meet latency and battery constraints. Consider using TensorFlow Lite or similar frameworks.
Define metrics (accuracy, latency, user engagement) and A/B testing methodology. Plan for monitoring, feedback loops, and incremental improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.