Dspy Framework

This is a framework that treats prompts like code instead of strings you manually tweak. You define input/output signatures, use modules like ChainOfThought or ReAct for reasoning patterns, then run optimizers that automatically improve your prompts using evaluation data. The optimizer tries different few-shot examples and picks what works best against your metric. It's worth learning if you're building production LLM systems where you need reproducible results and version control, or if you're tired of prompt engineering by hand. The learning curve is real since you're adopting a whole paradigm, but it pays off when you need to systematically improve accuracy across RAG pipelines or classification tasks with actual measurements instead of vibes.