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DSPy

What it is

DSPy (Declarative Self-improving Language Programs, Pythonically) is a framework for algorithmically optimizing LLM prompts and weights. It separates the flow of your program (modules) from the parameters (LM prompts and weights) of each step.

What problem it solves

Traditional LLM development involves manual prompt engineering ("prompt hacking"), which is brittle and doesn't scale. DSPy replaces this with a programming model where you define signatures and modules, and an optimizer automatically generates high-quality prompts or fine-tunes models to satisfy your requirements.

Where it fits in the stack

Framework

Typical use cases

  • Complex RAG Pipelines: Optimizing retrieval and generation steps together.
  • Multi-hop Question Answering: Managing state and logic across multiple LLM calls.
  • Self-Improving Agents: Automatically refining agent prompts based on few-shot examples.

Strengths

  • Programmatic Control: Define logic in Python rather than raw strings.
  • Automatic Optimization: Compilers (optimizers) like BootstrapFewShot generate effective prompts.
  • Model Agnostic: Easily switch between different LMs and re-optimize the pipeline.

Limitations

  • Learning Curve: Requires a shift in mindset from manual prompting to systematic programming.
  • Optimization Overhead: Running optimizers requires a training/validation dataset and can be time-consuming.

When to use it

  • When you are tired of manual prompt engineering.
  • When you need a robust, reproducible, and optimizable LLM pipeline.

When not to use it

  • For very simple, single-prompt applications.
  • If you don't have even a small dataset to use for optimization.

Getting started

Installation

pip install dspy

Minimal Python Example

import dspy
lm = dspy.OpenAI(model='gpt-3.5-turbo')
dspy.settings.configure(lm=lm)

class CoT(dspy.Signature):
    """Answer questions with chain of thought."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 10 and 50 words")

generate_answer = dspy.ChainOfThought(CoT)
pred = generate_answer(question="What is the capital of France?")
print(pred.answer)

Licensing and cost

  • Open Source: Yes (MIT License)
  • Cost: Free
  • Self-hostable: Yes

Sources / References

Contribution Metadata

  • Last reviewed: 2026-03-02
  • Confidence: high