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

Lucas Gren, Felix Dobslaw

TL;DR

The paper addresses the challenge of deploying GenAI in enterprise settings where traditional technical QA falls short of domain-specific quality and trust. It introduces the Expert Validation Framework (EVF), which positions domain experts as primary architects of system behavior through a four-stage lifecycle—Specification, Knowledge Foundation, Validation, and Production Monitoring—coupled with a Socratic dialogue mechanism to refine the system iteratively. Core contributions include three guiding principles (expert authority, systematic validation, and continuous monitoring) and practical insights from early implementations, such as the need for no-code test-management tools and transparency to build trust. The framework aims to enable trustworthy, expert-driven GenAI deployments in regulated or complex domains, with practical impact through living knowledge bases, continuous adaptation, and scalable expert participation.

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.

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

TL;DR

The paper addresses the challenge of deploying GenAI in enterprise settings where traditional technical QA falls short of domain-specific quality and trust. It introduces the Expert Validation Framework (EVF), which positions domain experts as primary architects of system behavior through a four-stage lifecycle—Specification, Knowledge Foundation, Validation, and Production Monitoring—coupled with a Socratic dialogue mechanism to refine the system iteratively. Core contributions include three guiding principles (expert authority, systematic validation, and continuous monitoring) and practical insights from early implementations, such as the need for no-code test-management tools and transparency to build trust. The framework aims to enable trustworthy, expert-driven GenAI deployments in regulated or complex domains, with practical impact through living knowledge bases, continuous adaptation, and scalable expert participation.

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.
Paper Structure (17 sections, 1 figure)

This paper contains 17 sections, 1 figure.

Table of Contents

  1. Introduction
  2. The Industrial Challenge
  3. The Implementation Reality Gap
  4. The Expert Knowledge Integration Problem
  5. The Expert Validation Framework
  6. Core Design Principles
  7. The Socratic Method in Practice
  8. Implementation Stages and Practical Insights
  9. Stage 1: Specification Through Expert Lens
  10. Stage 2: Knowledge Foundation as Living Asset
  11. Stage 3: Validation Beyond Accuracy
  12. Stage 4: Production Monitoring as Continuous Dialogue
  13. Lessons from Early Implementations
  14. The Expert Capability Challenge
  15. Trust Through Transparency
  16. ...and 2 more sections

Figures (1)

  • Figure 1: The Expert Validation Framework (EVF). The process relies on two critical feedback loops: the Socratic Refinement Loop during development and the Continuous Adaptation Loop from production back to specification.