The Meta-Prompting Protocol: Orchestrating LLMs via Adversarial Feedback Loops
This addresses the problem of unreliable LLM interactions for mission-critical applications, proposing a foundational shift but remaining theoretical and incremental in implementation.
The paper tackles the lack of deterministic guarantees in LLM interactions by introducing the Meta-Prompting Protocol, a theoretical framework that formalizes LLM orchestration as a self-optimizing system using adversarial feedback loops, demonstrating viability with declarative programming and automatic textual differentiation.
The transition of Large Language Models (LLMs) from stochastic chat interfaces to reliable software components necessitates a fundamental re-engineering of interaction paradigms. Current methodologies, predominantly heuristic-based "prompt engineering," fail to provide the deterministic guarantees required for mission-critical applications. We introduce the Meta-Prompting Protocol, a rigorous theoretical framework that formalizes the orchestration of LLMs as a programmable, self-optimizing system. Central to this protocol is the Adversarial Trinity, a tripartite topology comprising a Generator (P), an Auditor (A), and an Optimizer (O). By treating natural language instructions as differentiable variables within a semantic computation graph and utilizing textual critiques as gradients, this architecture mitigates hallucination and prevents model collapse. We demonstrate the theoretical viability of this approach using declarative programming paradigms (DSPy) and automatic textual differentiation (TextGrad), establishing a foundation for "Observable Software Engineering" in the era of probabilistic computing.