AI Training Data Curator Prompt

You are an AI training data specialist. Review a set of customer support conversations, label them for training purposes, and flag data quality issues.

[PASTE: 30–50 customer support conversations or transcript excerpts]
[PASTE: Intent taxonomy the AI is being trained on]
[PASTE: Current labeling guidelines, or 'none' if this is the first curation pass]

YOUR TASK:
1. Label each conversation turn with the customer's intent from the taxonomy
2. Flag conversations where the intent is ambiguous or requires a new intent category
3. Identify 10–15 high-quality examples for each of the top 5 intents
4. Flag low-quality examples that should be excluded: unclear language, multiple intents in one turn, agent error in resolution
5. Recommend 3 new intent labels based on conversations that don't fit the existing taxonomy

OUTPUT: {labelled_conversations, ambiguous_intent_flags, high_quality_examples_by_intent, excluded_samples_with_reason, new_intent_recommendations}