Understanding Intermittent Failures in LLMs

Intermittent failures in large language model (LLM) applications often stem from the stochastic nature of their underlying processes, challenging the assumption that identical inputs yield identical outputs. This unpredictability arises from factors like floating-point arithmetic variations and the inherent structural limitations of Transformers, such as the "lost in the middle" phenomenon, which can lead to missed context in larger datasets. Retrieval systems also contribute to inaccuracies when vector spaces become crowded, causing models to retrieve misleading information. Additionally, changes in model versions can introduce semantic drift, altering how prompts are interpreted. Mitigation strategies include combining dense vector retrieval with sparse keyword search, reranking retrieved results, and employing robust observability and evaluation tools to identify and address these issues effectively. Emphasizing resilience over determinism, developers are encouraged to implement strategies like hybrid search and robust failure handling mechanisms to manage uncertainty and maintain output quality.