These two academic papers introduce novel programming models aimed at systematically optimizing complex AI systems, particularly those built using Large Language Models (LLMs). The first source presents DSPy, a framework that abstracts traditional, hard-coded LLM pipelines into parameterized, declarative modules that can be automatically optimized using a compiler and teleprompters, demonstrating superior performance compared to hand-crafted prompts on tasks like math word problems. The second source introduces TEXTGRAD, a general optimization framework that utilizes LLMs to generate and propagate natural language gradients—textual feedback—through computation graphs, applying this "textual differentiation" approach successfully across diverse domains, including prompt optimization, code refinement, and scientific applications like molecular and medical treatment plan design. Both works highlight the shift from relying on expert prompt engineering to employing systematic, programmatic optimization techniques for compound AI systems.Sources:October 5, 2023DSPY: COMPILING DECLARATIVE LANGUAGE MODEL CALLS INTO SELF-IMPROVING PIPELINEShttps://arxiv.org/pdf/2310.03714June 11, 2024TextGrad: Automatic “Differentiation” via Texthttps://arxiv.org/pdf/2406.07496