DSPy

Most language model-based applications today rely on prompt templates hand-tuned for specific tasks. DSPy, a framework for developing such applications, takes a different approach that does away with direct prompt engineering. Instead, it introduces higher-level abstractions oriented around program flow (through modules that can be layered on top of each other), metrics to optimize towards and data to train or test with. It then optimizes the prompts or weights of the underlying language model based on those defined metrics. The resulting codebase looks much like the training of neural networks with PyTorch. We find the approach it takes refreshing for its different take and think it's worth experimenting with.