dspy

DSPy Framework

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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

Core Philosophy

DSPy (Declarative Self-improving Python) shifts focus from manual prompt engineering to programming language models. Treat prompts as code with:

• Declarative signatures defining inputs/outputs
• Automatic optimization via compilers
• Version control and systematic testing
• Reproducible results across model changes

Key Principle: Don't write prompts manually—define task specifications and let DSPy optimize them.

Core Concepts

Signatures: Defining Task Interfaces

Signatures specify what your LM module should do (inputs → outputs) without saying how.

Basic Signature:

import dspy

# Inline signature (quick)
qa_module = dspy.ChainOfThought("question -> answer")

# Class-based signature (recommended for production)
class QuestionAnswer(dspy.Signature):
    """Answer questions with short factual answers."""

    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

# Use signature
qa = dspy.ChainOfThought(QuestionAnswer)
response = qa(question="What is the capital of France?")
print(response.answer)  # "Paris"

Advanced Signatures with Type Hints:

from typing import List

class DocumentSummary(dspy.Signature):
    """Generate concise document summaries."""

    document: str = dspy.InputField(desc="Full text to summarize")
    key_points: List[str] = dspy.OutputField(desc="3-5 bullet points")
    summary: str = dspy.OutputField(desc="2-3 sentence summary")
    sentiment: str = dspy.OutputField(desc="positive, negative, or neutral")

# Type hints provide strong typing and validation
summarizer = dspy.ChainOfThought(DocumentSummary)
result = summarizer(document="Long document text...")