Design query generation to maximize CTR

Q: Design query generation to maximize CTR

This is a ML System Design interview question from TikTok for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

System Design: End-to-End Query Generation to Maximize CTR

Context

You are designing the query-generation layer for a consumer search/recommendation product with a search box and a highly dynamic content feed. The system should:

Generate user-facing query suggestions (e.g., autocomplete, related queries) that users can click.
Optionally rewrite/expand the user’s typed query into internal retrieval queries to fetch better candidates.

Assume scale is very large, traffic is global and mobile-first, and content is short-form, fast-evolving, and safety-sensitive.

Task

Design an end-to-end system that maximizes click-through rate (CTR) for query suggestions while protecting user experience and safety. Address:

Goals, constraints, and metrics
Data sources and feature engineering
Candidate generation and ranking models
Exploration–exploitation (e.g., bandits), feedback loops, and debiasing
Offline evaluation (splits, counterfactual evaluation) and online A/B testing
Real-time inference architecture, latency/throughput/SLA, and scalability
Personalization, cold start, and diversity controls
Safety/abuse and privacy compliance
Diagrams or pseudocode for critical components
How you would iterate after launch

Design query generation to maximize CTR

System Design: End-to-End Query Generation to Maximize CTR

Context

Task

Solution

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