A deep dive into managing large language models reveals significant advancements in privacy-preserving analytics and comprehensive evaluation strategies. One major innovation is Google's Provably Private Insights (PPI) system, an original work that leverages LLMs, Differential Privacy, and Trusted Execution Environments to provide confidential, population-level insights into on-device generative AI usage, ensuring robust user data privacy through an open-sourced and verifiable framework. Complementing this, expert reviews critically examine leading AI evaluation tools such as Langsmith, Braintrust, and Arize Phoenix, guiding teams to select solutions based on their technical stack and maturity while stressing the importance of human-in-the-loop workflows and transparency over mere feature lists. Further detailed are battle-tested strategies for effective LLM product evaluation, advocating for rigorous error analysis, human judgment, and specific binary evaluations as paramount to uncovering and addressing actual failure modes in complex RAG and agentic workflows, rather than relying solely on generic metrics.