description DSPy Overview
DSPy (Declarative Self-improving Language Programs) represents a paradigm shift in LLM development. Instead of manual prompt engineering, DSPy allows developers to define the logic of an AI program and then 'compile' it into optimized prompts based on a provided dataset. It treats LLM calls as differentiable modules, enabling systematic optimization of complex pipelines (like RAG or multi-hop reasoning) through automated search and training.
help DSPy FAQ
What is a DSPy Signature?
A Signature declares the inputs and outputs of a language-model task instead of hard-coding the full prompt text. DSPy modules such as Predict, ChainOfThought, and ReAct use that declaration when calling a model.
What does compiling a DSPy program actually change?
Compilation uses examples and a metric to improve a program's instructions, few-shot demonstrations, or sometimes model weights. Optimizers such as BootstrapFewShot, MIPROv2, and GEPA explore different ways to raise the selected metric.
Does DSPy work only with OpenAI models?
No. DSPy is designed to work with multiple language-model providers and can be configured for hosted or local models through supported clients.
How much training data does a DSPy optimizer need?
Some optimizers can begin with as few as 5 or 10 examples, according to the official documentation. Reliable optimization still requires a meaningful metric and a separate evaluation set so the program is not judged only on its training examples.
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