Beyond the Hype: Building Trustworthy and Reliable LLM Applications with Guardrails

Generative AI is driving a broad range of solutions, from chatbots and search tools to enterprise support systems. With GPT-4, ChatGPT, and new open-source developments appearing every week, there has never been a more interesting time to work with Large Language Models. However, organizations now face critical challenges: preventing data leaks, blocking hostile prompts, and ensuring brand or organizational policies remain enforced. This session explores concrete safety practices that help teams build more secure and consistent AI-based applications. Real-life examples will highlight the types of vulnerabilities encountered in customer support chatbots, internal knowledge bases, and automated content workflows. Using frameworks like LangChain4J, Hugging Face, and OpenAI, participants will learn to: Monitor and filter inputs and outputs to deter unauthorized content and malicious activities. Protect private data with targeted access controls, making sure sensitive information remains confidential. Address risks in retrieval-based tasks that could compromise data accuracy. Maintain responsible permissions and ongoing reviews so systems stay within intended parameters after release. Attendees will see live demonstrations of protective measures and gain practical insights for applying these methods throughout product development. The session closes with a look at the shifting landscape of LLM safety and the strategies needed to adapt as more sophisticated threats arise. It is intended for developers, security experts, and project leads seeking to deploy AI-driven features while preserving user and organizational security.