Mitigating LLM Failure Modes in Production

Deploying Large Language Models (LLMs) into production environments introduces unique challenges that traditional software testing cannot fully address. From unexpected hallucinations to security vulnerabilities, understanding how these models fail is the first step toward building robust AI applications. This text-based course guides you through the critical vulnerabilities of LLMs in real-world scenarios and teaches you how to systematically monitor, detect, and resolve them. You will transition from deploying unpredictable models to engineering resilient, secure, and consistent AI-driven systems. What you will learn: 1. Understand foundational LLM concepts and why production environments trigger unique failure states. 2. Identify critical vulnerabilities including hallucination, sycophancy, and prompt injection. 3. Implement modern mitigation strategies such as retrieval-augmented generation (RAG) and guardrails. 4. Detect model drift, inconsistency, and silent failures using modern evaluation patterns. 5. Design secure prompt templates and input validation flows to prevent adversarial attacks. 6. Configure basic monitoring and logging setups to track LLM behavior over time. You will start by mastering key terminology and the core mechanics of LLM behavior before moving into detailed analyses of specific failure modes. Through clear written explanations and structured code snippets, you will learn practical mitigation patterns and testing workflows to keep your applications stable. This course is designed for software developers, data practitioners, and technical product managers who are new to LLM operations. No prior experience with production AI systems is required, and a basic understanding of programming concepts is helpful. Start reading today to make your production language models reliable, predictable, and secure.