Case Study
Prediction Guard De-Risks LLM Applications at Scale
Prediction Guard effectively de-risks large language model applications at scale by providing robust security, reliability, and governance guardrails that prevent hallucinations and prompt injections. Critical to achieving high-speed performance and cost-efficiency, advanced Intel software development tools and optimization libraries—such as the Intel Extension for PyTorch and Optimum Habana—empowered the platform to seamlessly fine-tune and run complex AI models. By leveraging powerful Intel software and high-performance processor architectures, organizations can securely deploy generative AI solutions at scale, maintaining strict compliance and predictable infrastructure costs without sacrificing execution speed.
