Common Failure Modes of LLMs in Production
Explore common failure modes in production large language models such as hallucination, sycophancy, prompt injection vulnerability, refusal to answer, and output inconsistency. Learn how each failure arises, its risks, and practical detection and mitigation approaches to improve reliability and trust in real-world applications.
The previous lesson introduced alignment pillars and showed how misalignment produces problems like hallucination, harmful content, off-topic responses, and sycophancy. This lesson shifts from why models go wrong to how they go wrong in practice. Even well-aligned models exhibit predictable failure patterns once they face the volume, variety, and unpredictability of real-world production traffic. Each pattern demands its own detection and mitigation approach. The five failure modes covered here are hallucination, sycophancy, prompt injection susceptibility, refusal to answer, and inconsistency. Understanding these modes is essential groundwork before tackling the data complexity challenges in the next lesson.
The following visual maps out these five failure modes and their key characteristics at a glance.
Each branch in this map represents a distinct category of production failure, and the sub-nodes highlight the specific symptoms that surface in enterprise deployments.
Hallucination and sycophancy
How hallucination works in production
Hallucination is not random noise. It follows recognizable patterns driven by how language models generate text. When a model encounters a query that falls outside or at the edges of its training distribution, it fills knowledge gaps with plausible-sounding fabrications. It may invent citations to academic papers that do not exist, generate statistics with false precision, or present uncertain information with the same confident tone it uses for well-established facts. The risk increases sharply ...