I started with the obvious stuff, n-gram language models, prefix trees, that kind of thing, but pretty quickly realized the scope was way bigger than I'd framed it.
Start by clarifying requirements and scale, then outline a high-level architecture that separates offline model training from online serving. Focus on the core components: candidate generation, ranking, and latency optimization, while discussing trade-offs between model complexity and response time.
Pro tip: Emphasize the importance of latency and personalization: users expect suggestions in milliseconds, so caching and efficient data structures are critical. Also, mention how you would handle cold-start and privacy concerns, as these are often overlooked.
Ask about expected scale (QPS, latency), data sources (user history, global corpus), and privacy constraints. Define success metrics like suggestion acceptance rate.
Propose a two-part system: offline training of language models (e.g., n-gram, neural) and online serving with a fast lookup. Include components like candidate generator, ranker, and cache.
Describe how to generate candidates efficiently (e.g., trie, inverted index, or neural beam search) and rank them using a lightweight model (e.g., logistic regression or small neural net) to balance quality and latency.
Discuss techniques like caching frequent prefixes, sharding, and using approximate nearest neighbor search. Mention trade-offs between model size and inference speed.
Explain how to evaluate offline (perplexity, BLEU) and online (A/B testing, acceptance rate). Mention monitoring and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.