This paper introduces a scalable evaluation framework for Retrieval-Augmented Generation (RAG) based large language model (LLM) agents, operationalizing the concept of "using AI to test AI." At the heart of the framework is an LLM-as-judge approach, where a generative model systematically evaluates retrieval quality and output accuracy by comparing results to expert-defined expectations. By leveraging AI to perform the evaluation, the framework enables dramatically faster and more efficient hill climbing—the iterative optimization of prompts, system configurations, and model components. Instead of relying on slow, manual review cycles, AI-driven scoring provides immediate feedback, making it possible to explore larger design spaces and converge on high-performing solutions more quickly. Key advantages of this approach include:
Accelerated evaluation loops, powering rapid experimentation and improvement. Standardized, repeatable assessments, enabling consistent comparisons across iterations. Reduced human bias, especially in subjective metrics like relevance and fluency. High scalability, supporting automated testing across vast datasets and configuration variants. Domain-specific alignment, ensuring evaluations reflect nuanced goals such as groundedness, factual accuracy, and code generation quality.
The framework evaluates both retrieval and generation components across multiple dimensions and supports continual benchmarking throughout the AI lifecycle. By embedding intelligence into the testing process itself, this methodology not only improves system robustness and reliability—it significantly increases the efficiency and effectiveness of hill climbing in AI system development.
