// Abstract
Chaining language model (LM) calls as composable modules is fueling a powerful way of programming. However, ensuring that LMs adhere to important constraints remains a key challenge, one often addressed with heuristic “prompt engineering”. We introduce LM Assertions, a new programming construct for expressing computational constraints that LMs should satisfy. We integrate our constructs into the recent DSPy programming model for LMs, and present new strategies that allow DSPy to compile programs with arbitrary LM Assertions into systems that are more reliable and more accurate. In DSPy, LM Assertions can be integrated at compile time, via automatic prompt optimization, and and/or at inference-time, via automatic self-refinement and backtracking.
// Bio
Arnav recently-graduated undergrad in CS at UC Berkeley. Researcher with Stanford NLP Group, contributing to DSPy framework
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