Metrics that matter for GenAI evaluation

Generative AI is moving into critical business workflows, but many evaluation practices are still stuck in the past.

Traditional metrics such as accuracy, fluency, and benchmark scores can make a GenAI system look strong on paper while it disappoints users, creates risk, or fails to deliver business value in production.

This white paper explains how to evaluate GenAI systems based on what actually matters in real-world use: whether they help users complete tasks, stay grounded in the right information, behave safely, perform reliably, and create measurable business impact.

It introduces a practical framework for GenAI evaluation across five quality dimensions — Utility, Truth, Safety, Reliability, and Experience — and shows how metric selection changes for conversational AI, RAG systems, and multi-agent architectures.

What you'll learn from this white paper

Why traditional AI metrics can misrepresent GenAI performance
How to define GenAI quality beyond accuracy, fluency, and benchmark scores
Which metrics matter for conversational AI, RAG, and agentic systems
How to connect technical quality signals to business outcomes such as cost, risk, adoption, and trust
How to build a continuous evaluation approach for GenAI systems in production

Download the white paper to learn how to measure GenAI systems that are not just accurate, but useful, safe, reliable, and trusted.

White paper

Metrics that matter for GenAI evaluation

Authors

Anamika Mukhopadhyay

Deepshikha

What you'll learn from this white paper

Authors

Anamika Mukhopadhyay

Deepshikha