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Operationalizing large language models (LLMs) for custom, repetitive datapipelines is challenging, particularly due to their unpredictable andpotentially catastrophic failures. Acknowledging the inevitability of theseerrors, we focus on identifying when LLMs may be generating incorrect responseswhen used repeatedly as part of data generation pipelines. We present SPADE, amethod for automatically synthesizing assertions that identify bad LLM outputs.SPADE analyzes prompt version histories to create candidate assertion functionsand then selects a minimal set that fulfills both coverage and accuracyrequirements. In testing across nine different real-world LLM pipelines, SPADEefficiently reduces the number of assertions by 14% and decreases falsefailures by 21% when compared to simpler baselines.

SPADE: Synthesizing Assertions for Large Language Model Pipelines