Model Evaluation with NIMBUS Uno | RAG Validation, Retrieval Quality & Performance Metrics

Description

Evaluating LLM and GenAI model performance requires more than accuracy checks, it demands a full, multi-layer assessment of reasoning, relevance, retrieval, and ranking quality. In this video, discover how Nimbus Uno delivers an end-to-end framework for advanced Model Evaluation, enabling teams to validate both standalone LLMs and complex RAG pipelines with precision. NIMBUS allows you to evaluate multiple models in parallel using both benchmark datasets like MMLU and HelI-SWAK or custom datasets generated within Nimbus. For every query, Nimbus computes detailed performance metrics including accuracy, precision, coherence, contextual relevance, and more. You can visually compare model responses against ground truth answers, analyze strengths and weaknesses, and determine which model performs most reliably for your domain. The platform also includes a powerful RAG Validation and Retrieval Quality module. Here, Nimbus maps the prompt, retrieved context, and reference documents side-by-side, computing metrics like Precision@K, Recall@K, and nDCG@K to assess ranking quality and relevance. Every component of the retrieval pipeline becomes traceable and auditable, ensuring your model operates with the most accurate context before generating outputs. NIMBUS Uno provides a comprehensive, audit-ready framework for Model Evaluation, empowering organizations to deploy GenAI systems with confidence, transparency, and reliability. Contact us today for a demo: https://www.solytics-partners.com/products/nimbus-uno #ModelEvaluation #RAGValidation #RetrievalQuality #PerformanceMetrics #LLMTesting #AIGovernance #ModelValidation #NimbusUno #SolyticsPartners