Thanks for making it all the way to the end of this course. Generative AI is a really powerful technology. It's a fundamentally non-deterministic programing paradigm. All of the amazing applications you see, whether it's retrieval augmented generation, or chatbots or agents to bring them to production, you'll need to make sure that they run with the level of reliability that we've come to expect from deterministic software. Guardrails are an important tool to do this, and can help you make sure that you deal with both the average case, as well as the worst case behavior of very complex genAI workflows. Validation is important in making sure that any genAI application you build works as expected, and avoids the failure cases you explored in this course, as well as many, many more failure cases that you'll run into in the wild. You've learned the general process of building guardrails, which is identifying problems, building validators to detect when those problems occur, and then taking corrective actions to mitigate those problems. Always using these guardrails around your LLM call will help you make sure that your AI is reliable and safe. So please take the time to explore the validators that are available on the guardrails hub, and even consider building your own guardrails and sharing them with the community through our open-source projects. I can't wait to see what you're going to build.