DSPy Lab
·1 min
A hands-on course for understanding LLM programs as systems that can be specified, evaluated, and optimized rather than as collections of hand-written prompts.
The lab progresses from signatures and modules through evaluation, few-shot optimization, instruction optimization, feedback-driven optimization, agents, inference-time strategies, and observability. The recurring mental model is deliberately close to machine learning: define the computation graph, define the metric, then optimize the program against data.
The repository is organized as self-contained notebooks, including a capstone on HotPotQA and an under-the-LLM-layer deep dive.