DSPy Framework

progressive_disclosure: entry_point: summary: "Declarative framework for automatic prompt optimization treating prompts as code" when_to_use: - "When optimizing prompts systematically with evaluation data" - "When building production LLM systems requiring accuracy improvements" - "When implementing RAG, classification, or structured extraction tasks" - "When version-controlled, reproducible prompts are needed" quick_start: - "pip install dspy-ai" - "Define signature: class QA(dspy.Signature): question = dspy.InputField(); answer = dspy.OutputField()" - "Create module: qa = dspy.ChainOfThought(QA)" - "Optimize: optimizer.compile(qa, trainset=examples)" token_estimate: entry: 75 full: 5500