Large Language Models: Navigating Risks, Bias, and Security

As large language models become deeply integrated into modern software, understanding their inherent limitations and security vulnerabilities is more critical than ever. This text-based course equips you with the foundational knowledge to spot, analyze, and mitigate key risks in AI systems. You will transition from simply utilizing models to responsibly deploying and securing them against real-world threats. What you'll learn: - Understand foundational concepts of model alignment, bias, and toxic outputs - Identify and prevent common generation errors like hallucinations and factual inconsistencies - Analyze modern security vulnerabilities such as prompt injection and data poisoning - Apply privacy-preserving techniques to protect sensitive training data and user inputs - Implement safety guardrails and evaluation frameworks within your AI workflows Starting with fundamental definitions of safety and alignment, this course guides you through structured written analyses of real-world vulnerabilities and practical mitigation strategies. Designed for beginners, developers, and product managers looking to build responsible AI systems, this course requires no prior machine learning experience. Begin reading today to build safer, more reliable AI applications.