prompt-calculus

What problem does it solve?

This formalism provides a compact framework to reason about LLM prompts, their evaluation semantics, and how prompt functions compose and affect outputs.

Core Features & Use Cases

• Evaluation kernel and context modeling: defines how prompts map to output distributions and how context modifies evaluation.
• Prompt functions and composition: formalizes applying prompts as functions and composing them (G ∘ F) to build complex behaviors.
• Iterated self-application and fixed points: explains how prompts can be applied repeatedly, convergence properties, and quines.

Quick Start

Apply a simple prompt function to a sample input and trace the resulting output distribution using the evaluation kernel.