As generative AI systems rapidly transition from research labs to real-world applications, ensuring reliability, safety, and trust is more critical than ever. In this session, we explore how to balance innovation with responsibility by diving into the entire lifecycle of AI model development—from selecting the right model and crafting effective prompts to fine-tuning on private datasets and conducting rigorous, comprehensive testing. We will discuss how to detect and mitigate pitfalls such as hallucinations and factually irrelevant outputs, highlighting the importance of both manual red-teaming approaches and automated benchmarking tools. Through real-world examples, you’ll see where AI models can fail and how to address vulnerabilities. We will also introduce new strategies for continuous monitoring and feedback, ensuring your AI stays aligned and resilient in production. Our live demo will showcase automated evaluation models and real-time red-teaming, equipping you with the knowledge to deploy AI solutions responsibly—so you can innovate without compromising trust or integrity.