Validation is what separates a demo from a product. Before you scale a GenAI system, you need a systematic way to evaluate quality, test behavior, and monitor performance.

This step combines three topics that work together:

• Evaluation: define what “good” means for your use case and measure it.
• Testing: check behavior in realistic scenarios, including failure modes.
• Monitoring: track performance in production so problems are detected early and improvements are guided by evidence.

Evaluation of generative AI systems

Evaluation of generative AI systems is different from evaluating traditional ML. Outputs are flexible, and multiple answers can be “acceptable,” which makes evaluation more nuanced, especially in user-facing products.

A helpful way to stay oriented is to remember that evaluation depends on two things at the same time:

• System design choices: model selection, LLM parameters, prompt templates, RAG parameters, fine-tuning and training data
• Use case demands: summarization, Q&A, chatbots, code generation, translation, and similar tasks

Use the diagram as a reminder that when quality changes, you should check both sides: what changed in the system, and what the use case demands.

What to evaluate

Use a small set of quality dimensions that match your system design:

• Faithfulness: does the answer match the retrieved source information?
• Answer relevancy: does the answer address the question without irrelevant filler?
• Context precision (RAG): is retrieved context relevant or noisy?
• Context recall (RAG): did the system retrieve everything needed?

Monitoring in AI systems