The Expert Validation Framework (EVF): Enabling Domain Expert Control in AI Engineering

2026-01-18

AI Summary

This work addresses the widespread absence of systematic quality assurance mechanisms in enterprise deployments of generative AI systems, which hinders the establishment of organizational trust. To bridge this gap, the paper proposes a novel four-stage quality assurance framework centered on domain experts, encompassing structured specification definition, system construction, expert-driven testing and validation, and continuous production monitoring. For the first time, this approach deeply integrates domain expertise throughout the entire generative AI engineering lifecycle. By doing so, it effectively reconciles the capabilities of generative AI with organizational trust requirements, ensuring authoritative expert oversight and high-quality outputs across diverse application scenarios, thereby significantly enhancing enterprises’ capacity for trustworthy deployment of generative AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Generative AI (GenAI) systems promise to transform knowledge work by automating a range of tasks, yet their deployment in enterprise settings remains hindered by the lack of systematic quality assurance mechanisms. We present an Expert Validation Framework that places domain experts at the center of building software with GenAI components, enabling them to maintain authoritative control over system behavior through structured specification, testing, validation, and continuous monitoring processes. Our framework addresses the critical gap between AI capabilities and organizational trust by establishing a rigorous, expert-driven methodology for ensuring quality across diverse GenAI applications. Through a four-stage implementation process encompassing specification, system creation, validation, and production monitoring, the framework enables organizations to leverage GenAI capabilities while maintaining expert oversight and quality standards.

Problem

• Generative AI
• quality assurance
• domain expert
• enterprise deployment
• trust

Innovation

• Expert Validation Framework
• Generative AI
• Domain Expert Control
• Quality Assurance
• AI Engineering