Safely Executing LLM Code

In this episode, AI experts Bradley Arsenault and Justin Macon dive deep into the challenges and best practices for safely executing code generated by large language models in a production environment. They discuss key security considerations, containerization techniques, static/dynamic code analysis, and error handling - providing valuable insights for anyone looking to leverage the power of LLMs while mitigating the risks of abuse by AI hackers.---Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.Check out PromptDesk.ai for an open-source prompt management tool.Check out Brad’s AI Consultancy at bradleyarsenault.meAdd Justin Macorin and Bradley Arsenault on LinkedIn.Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_linkHosted on Ausha. See ausha.co/privacy-policy for more information.