DSPy — Programming Foundation Models Declaratively

What is DSPy?

DSPy is a framework that replaces hand-written prompts with modular, compilable programs. Instead of tweaking prompt strings, you define what your AI pipeline should do declaratively — then DSPy optimizes the prompts automatically through compilation. It treats LLM calls as optimizable modules, similar to how PyTorch treats neural network layers.

Answer-Ready: DSPy is a framework for programming (not prompting) LLMs. Define AI pipelines declaratively, compile them into optimized prompts automatically. Created at Stanford NLP. Replaces prompt engineering with systematic optimization. 22k+ GitHub stars.

Best for: AI engineers building reliable LLM pipelines. Works with: OpenAI, Anthropic Claude, local models. Setup time: Under 3 minutes.

Core Concepts

1. Signatures (Define I/O)

# Simple signature
classify = dspy.Predict("sentence -> sentiment")

# Detailed signature
class FactCheck(dspy.Signature):
    claim = dspy.InputField(desc="A factual claim to verify")
    evidence = dspy.OutputField(desc="Supporting or refuting evidence")
    verdict = dspy.OutputField(desc="True, False, or Uncertain")

2. Modules (Build Pipelines)

class RAGPipeline(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=5)
        self.generate = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

3. Optimizers (Compile Prompts)

from dspy.teleprompt import BootstrapFewShot

# Provide training examples
trainset = [
    dspy.Example(question="...", answer="...").with_inputs("question"),
]

# Compile: auto-generate optimized prompts
optimizer = BootstrapFewShot(metric=my_metric, max_bootstrapped_demos=4)
compiled_rag = optimizer.compile(RAGPipeline(), trainset=trainset)

4. Metrics

def my_metric(example, prediction, trace=None):
    return prediction.answer.lower() == example.answer.lower()

Why DSPy over Prompt Engineering?

FAQ

Q: Does it work with Claude? A: Yes, supports Anthropic Claude via dspy.LM("anthropic/claude-sonnet-4-20250514").

Q: How is it different from LangChain? A: LangChain chains manual prompts together. DSPy optimizes prompts automatically through compilation — you define the task, DSPy figures out the best prompt.

Q: Is it production-ready? A: Yes, used by companies for production RAG, classification, and extraction pipelines.