PROFESSIONAL • LLM and RAG Production

Evaluation Playbook for personalized recommendation prototype #19

This lesson focuses on prevent data leakage using a practical personalized recommendation prototype scenario. You will apply commands: docker build -t ai-service . | kubectl apply -f deployment.yaml | prometheus + grafana dashboards. The code example demonstrates a concrete workflow aligned with this lesson objective, not generic filler.