Generative AI (GenAI) and Large Language Models (LLMs) have become pivotal in enhancing enterprise applications. Patterns like retrieval augmented generation (RAG), querying SQL databases, and agentic workflows are gaining popularity in the building of next-gen applications. What’s needed, however, is a unified approach for enterprises to evaluate these GenAI-enhanced applications built using these models through a rigorous evaluation.
In this white paper, we present an enterprise-focused Evaluation Framework designed by Persistent to assess the performance of GenAI-based applications on unstructured and structured data utilizing both black box and white box testing approaches, with the capability to assess agent trajectory for agentic workflows.
Key Highlights
- Learn how to determine the right metrics by which to evaluate data from GenAI-enhanced applications.
- Review how the Evaluation Framework engineered by Persistent is designed for enterprises to methodically assess GenAI application performance, automate the creation of test data, and evaluate answer quality using intuitive application-oriented metrics.
- Discover how to utilize our framework to drive ongoing application enhancement and value.
