Conditional Prompt Branching

via “reactive multi-turn prompting with conditional branching”

Programming language for constrained LLM interaction.

Unique: Exposes template variables to Python context after generation, enabling imperative control flow to branch on intermediate outputs. The execution model maintains full prompt history and re-sends it with each new generation, creating a reactive prompt-building pattern.

vs others: More flexible than static prompt templates because logic can branch dynamically based on model outputs; simpler than agent frameworks because control flow is explicit Python, not autonomous loops.