Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding
TL;DR
TRACE is a framework that automates the enrichment of e-commerce product catalogs using agentic Large Language Models (LLMs). It achieved 98.2% accuracy in attribute proposals and increased checkout conversion by 0.48%.
✦ Why It Matters
Implement TRACE to enhance your e-commerce catalog and boost conversion rates today.
Key Takeaways
- Utilize TRACE for automated catalog enrichment.
- Leverage multimodal evidence for attribute proposals.
- Verify attribute values with a JudgeAgent.
- Display enriched attributes to improve conversion rates.
How It Works
TRACE employs two agents: the ScoutAgent collects evidence from multiple sources to suggest attribute values, while the JudgeAgent assesses these suggestions against the evidence to ensure accuracy before publication.