ByteDance · TikTok
AI Search Product Manager
Used foundation-model capabilities to reshape TikTok search, improving AI answer cards across intent detection, visual relevance, blended ranking, and citation interactions. I defined where AI cards added value, built the evaluation framework, and co-designed strategies with algorithm teams from serving and ranking through citations. I also structured large volumes of verbatim user feedback into actionable product and strategy directions, helping launch multiple improvements across global markets.
My role
Defined the appropriate scope for AI search cards, built evaluation frameworks, and co-developed algorithm strategies across the end-to-end experience from serving and ranking to citations.
Intent detection
- Used random-query annotation to identify scenarios where AI cards added value, created a detailed intent taxonomy and test set, and designed the intent evaluation framework.
- For encyclopedic entities, combined external encyclopedias, foundation-model knowledge, and on-platform video content.
- Raised both intent precision and recall above 90%.
Visual relevance & ranking
- Defined image and video use cases, aligned visual evaluation standards, and systematically improved image prompts, raising the end-to-end perfect-score rate to 70% while lowering false positives to 10%.
- Designed a lightweight two-column blended layout and helped define its evaluation framework. The strategy launched globally, increasing AI-card coverage by 6% and card CTR by 11%.
Citation experience
- Redesigned citations through a large-scale survey, verbatim user feedback, and competitive analysis—surfacing authoritative sources and domains, distinguishing on-platform from external sources, and simplifying the citation panel and path.
- Increased citation CTR by 300%.
What I learned
Search is ultimately about efficiency and trust. Foundation models can make search faster, but that efficiency only matters when users can see the sources and trust the answer.
Next experience
Baidu · ERNIE