Enterprises are rapidly deploying AI to stay competitive, but many are overlooking core security principles in the rush to innovate. Large language models pose multifaceted threats unlike anything in conventional software, as attackers exploit unpredictable outputs, prompt injections, and data exfiltration routes.

Quantization is a method that reduces the precision of a model’s weights and activations, leading to more efficient use of disk storage, less memory usage, and fewer compute requirements. This approach holds great promise for large language models (LLMs) looking to optimize performance on smaller hardware.

New research reveals that integrating fine-tuning with in-context learning empowers large language models to tackle complex tasks more efficiently than ever before.