Spent the first few minutes just trying to scope it properly because 'auto-complete' could mean a search bar, a code editor, a messaging app, all different beasts.
Start by clarifying requirements (scale, latency, personalization, offline vs online) and then walk through the end-to-end system: data collection, candidate generation, ranking, and serving. Emphasize trade-offs between model complexity, latency, and freshness, and discuss how you would evaluate and iterate on the system.
Pro tip: Show product sense by connecting technical choices to user experience—e.g., how latency directly impacts engagement, and how personalization can be balanced with trending queries. Also, mention fallback strategies for cold-start or low-confidence predictions.
Ask about scale (QPS, users), latency constraints, personalization needs, and data availability. Define success metrics (e.g., suggestion acceptance rate, latency).
Outline the main components: query logging, candidate generation (e.g., from historical queries, trending, personal history), ranking model, and serving layer with caching.
Describe how to generate candidates efficiently (e.g., prefix-based trie, embedding similarity) and rank them using a model (e.g., gradient boosted trees or neural network) that considers features like frequency, recency, user context.
Discuss serving infrastructure: in-memory stores, sharding, caching, and model inference optimizations (quantization, distillation) to meet latency SLAs.
Explain offline evaluation (A/B testing, metrics) and online monitoring. Discuss how to handle feedback loops and retrain models periodically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.