In this talk, we will dive into innovative methods to minimize AI hallucinations and significantly improve the accuracy of generative AI models through automated reasoning checks. As AI systems become increasingly prevalent across industries, ensuring the reliability and trustworthiness of their outputs is more critical than ever. Traditional AI models often produce plausible but incorrect information—known as hallucinations—posing challenges especially in regulated sectors where precision is essential.

We will explore how formal verification and automated reasoning techniques can be integrated into AI workflows to systematically validate outputs against domain-specific rules. This approach goes beyond probabilistic models by providing mathematically rigorous guarantees, enabling detection and prevention of factual inaccuracies with up to 99% verification accuracy.

Attendees will learn about the practical implementation of these automated reasoning checks, how they enhance AI guardrails, and the benefits of embedding them into AI services such as those offered on cloud platforms. The session will highlight real-world use cases and best practices for deploying AI solutions that demand high compliance, safety, and trust.

Whether you’re developing AI for finance, healthcare, or any domain where data integrity is paramount, this talk will equip you with the knowledge to build more reliable, verifiable, and responsible AI systems.