Measuring Quality in GenAI: Beyond Accuracy

How do you measure whether a Generative AI product is actually good?

Traditional software gives us relatively clear metrics: accuracy, latency, uptime, conversion, and errors. GenAI changes the equation. The output is probabilistic, open-ended, and often subjective — which makes “quality” much harder to define, measure, and improve.

In this episode, we explore how product managers can build a practical quality framework for GenAI products.

We look at questions such as:

• What does “quality” actually mean for a GenAI experience?
• Why traditional accuracy metrics often fall short
• The difference between model quality, response quality, and product quality
• How to evaluate correctness, relevance, helpfulness, groundedness, safety, and consistency
• Offline evaluation vs. online evaluation
• Building evaluation datasets and representative test cases
• Human evaluation, LLM-as-a-Judge, and automated evaluation
• How to handle subjective outputs and different user expectations
• Measuring hallucinations and factual reliability
• The role of task success and user outcomes in GenAI evaluation
• How product teams should think about quality trade-offs between quality, latency, and cost
• Designing a continuous evaluation loop as models and prompts evolve

The key shift is from asking “Is the model accurate?” to asking:

“Did the AI help the user accomplish what they were trying to do?”

For AI product managers, engineers, founders, and anyone building GenAI products, this episode provides a framework for turning the vague concept of “AI quality” into something that can actually be evaluated, monitored, and improved.

🎙️ AI Product Management Podcast

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