Shopping Experts: AI Training

United StatesRemote

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Requirements

The goal is to collect real-world professional scenarios that will help train AI models to handle complex advisory situations.
The objective of this research is to help AI understand real shopping situations — especially those involving nuances, trade-offs, and hidden details that completely change the required approach.
experience professionals - Gift consultants and event planners - Experienced shoppers who navigated a complex purchase decision VALIDATION CRITERIA Automatically rejected: Generic responses, use of AI, inconsistency across fields (detailed in one field, superficial in another), made-up cases without real domain knowledge.
The use of AI is not permitted.
experience in the field and knowledge of how situations actually work in practice.Required for success: a scenario that is resolved primarily through conversation or consultation.
EXPECTED TIME ~30 minutes per submission.Remember: The use of AI is not permitted
REQUIREMENTS - Individuals interested in contributing to AI development. - People who enjoy reflecting on their consumer journeys. - Those eager to share their insights on purchase processes. - Anyone keen to improve future advisory technologies. - Individuals with a willingness to articulate their experiences clearly.

COMPENSATION USD 12.00-220.00 for the interview READY TO PARTICIPATE? Start your paid interview now https://terac.com/interview/start/r/2a5bc9aa-c695-4902-8ab4-e94b3c9a007b?utm_source=ashby_listing_description

Welcome! We are conducting a research study with Shopping professionals.
CONVERSATION-ONLY SCENARIOS This form exclusively accepts scenarios that are resolved through a conversation or consultation.
Submissions that depend on digital tools should be submitted in the tool-supported scenario form instead.
WHO CAN PARTICIPATE - Personal shoppers and styling professionals - Retail buyers and merchandisers - E-commerce and customer
Generic or superficial responses will be rejected.
Submissions with clear hidden details and concrete constraints are significantly more valued.Approval criteria: Coherence between fields and the story, real and domain-specific details, at least 3 concrete constraints, and a clear hidden detail that changes the required approach.