PremAI's evaluation framework plays a crucial role in validating AI models before their deployment into production environments. The framework provides detailed insights into model performance through customizable rubric-based metrics, ensuring that AI models deliver consistent results beyond controlled test scenarios. PremAI addresses the unique evaluation challenges of large language models (LLMs) and small language models (SLMs), which traditional metrics like ROUGE and BLEU might not fully capture, by offering two core methodologies: Agentic Evaluation and Bring Your Own Evaluation (BYOE). Agentic Evaluation is designed for organizations lacking existing evaluation infrastructure, allowing them to create natural language-defined metrics that PremAI converts into comprehensive rubrics for model assessment. BYOE caters to enterprises with established evaluation systems, enabling them to evaluate models on their infrastructure while retaining results within the Prem ecosystem. Both approaches aim to ensure thorough evaluation lifecycle coverage, integrating seamlessly with data augmentation and fine-tuning processes to refine models continuously. PremAI's evaluation process includes metric creation and judge-based assessment, with results displayed transparently on an evaluation dashboard, and it supports organizations in identifying model strengths and weaknesses to facilitate targeted improvements.