Published : Oct 23, 2024
NOT ON THE CURRENT EDITION
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar.
Understand more
Oct 2024
Assess
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.
