Eryl Auto-Eval is an advanced AI framework that enhances Question-Answering systems by combining retrieval-augmented generation with automated evaluation. Traditional QA models often struggle with relevance, context depth, and self-improvement. Eryl solves this by using a multi-agent setup—an Answer Agent generates responses, while a Critic Agent evaluates them against key metrics like relevance, helpfulness, and completeness.

Through iterative feedback and metrics like Chunk Recall and Contextual Recall, Eryl ensures high-quality, context-aware answers. It continuously learns and improves, outperforming traditional systems. Eryl is scalable, reliable, and ideal for complex, cross-domain enterprise applications.