Mapping out potential conversation paths and logic
Generating high-quality training data is often the biggest bottleneck in building great AI. Synthetic Conversation Trees (SCTs) solve this by allowing you to map out possible interactions visually.
A Conversation Tree is a visual map of all the potential paths a conversation can take. It consists of:
Nodes: Specific states in the conversation (e.g., “User asks for refund”).
Edges: Transitions between states (e.g., “Agent asks for order number”).
Variables: Placeholders for dynamic data (e.g., order_id and customer_name wrapped in double curly braces) that ensure diversity in the generated output.
The visualizer allows you to ensure logic completeness. By mapping out the tree, you can easily spot missing edge cases or circular logic that would otherwise be difficult to identify in raw text datasets.
