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Chaining language model (LM) calls as composable modules is fueling a newpowerful way of programming. However, ensuring that LMs adhere to importantconstraints remains a key challenge, one often addressed with heuristic "promptengineering". We introduce LM Assertions, a new programming construct forexpressing computational constraints that LMs should satisfy. We integrate ourconstructs into the recent DSPy programming model for LMs, and present newstrategies that allow DSPy to compile programs with arbitrary LM Assertionsinto systems that are more reliable and more accurate. In DSPy, LM Assertionscan be integrated at compile time, via automatic prompt optimization, and/or atinference time, via automatic selfrefinement and backtracking. We report on twoearly case studies for complex question answering (QA), in which the LM programmust iteratively retrieve information in multiple hops and synthesize along-form answer with citations. We find that LM Assertions improve not onlycompliance with imposed rules and guidelines but also enhance downstream taskperformance, delivering intrinsic and extrinsic gains up to 35.7% and 13.3%,respectively. Our reference implementation of LM Assertions is integrated intoDSPy at https://github.com/stanfordnlp/dspy

DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines