Executive Summary
ProcessVenue developed a high-consistency Hindi intent classification dataset to enable robust AI-assistant prompt routing. The project labelled 5,000 Hindi user prompts across six core assistant intents. Through dual independent annotation and structured verification, the dataset achieved 77.22% IAA, demonstrating dependable supervision quality and stable intent boundaries suitable for training production-grade intent routers.
Objective
Create a low-noise Hindi intent dataset aligned with real assistant traffic to improve routing reliability and supervised model learning for multi-intent and constraint-rich user prompts.
Key Challenges
Hindi prompts in assistant environments frequently contain overlapping or mixed intents, making classification non-trivial. Major complexity drivers included:
Multi-clause instructions combining tasks in one prompt
Constraint-heavy language (tone, format, tool usage, style requirements)
In-prompt domain switching (e.g., educa…
Executive Summary
ProcessVenue developed a high-consistency Hindi intent classification dataset to enable robust AI-assistant prompt routing. The project labelled 5,000 Hindi user prompts across six core assistant intents. Through dual independent annotation and structured verification, the dataset achieved 77.22% IAA, demonstrating dependable supervision quality and stable intent boundaries suitable for training production-grade intent routers.
Objective
Create a low-noise Hindi intent dataset aligned with real assistant traffic to improve routing reliability and supervised model learning for multi-intent and constraint-rich user prompts.
Key Challenges
Hindi prompts in assistant environments frequently contain overlapping or mixed intents, making classification non-trivial. Major complexity drivers included:
Multi-clause instructions combining tasks in one prompt
Constraint-heavy language (tone, format, tool usage, style requirements)
In-prompt domain switching (e.g., educa…
