Small Business MBA

Model-Agnostic Protocol (MAP)

A Systematic Approach to LLM Response Reliability

Toaster is Gonna Toast! AI is best when you help it stay in line using proper prompt controls

Large Language Models (LLM) deliver inconsistent quality across sessions, hallucinate information with confidence, and exhibit concerning patterns of agreement regardless of factual accuracy. These reliability challenges create operational risk in environments where accuracy matters — compliance reporting, safety assessments, and strategic decision support.

The Model-Agnostic Protocol (MAP) addresses these issues through structured separation of response generation from quality assurance. MAP implements three coordinated modules that handle content creation, validation, and correction as distinct processes. This architectural approach enables systematic detection and mitigation of unreliable outputs while maintaining flexibility across different model providers.

Framework Architecture

MAP operates through three independent modules that work in sequence. The Generation Module focuses exclusively on task completion without attempting self-evaluation. The Validation Module audits generated content using separate prompts and potentially different model instances to avoid confirmation bias. The Correction Module orchestrates guided regeneration when validation…