---

name: dspy description: >- Optimize and build programmatic prompt systems with Stanford DSPy. Signatures, modules (Predict, ChainOfThought, ReAct), optimizer/teleprompter selection, compilation, caching, evaluation. Use when doing programmatic prompt optimization or building compiled prompt programs. Do not use this skill for unrelated requests; route to the nearest named specialist. license: MIT metadata: author: Magnus Hedemark version: 1.1.0 source: https://dspy.ai

DSPy Expert Skill

DSPy is a compiler for prompt programs, not a chain or RAG framework. You write Python programs with typed signatures and DSPy optimizes the prompts automatically.

⚠️ DSPy is NOT a chain framework. It does not use prompt | model | parser. It does not have LCEL. DSPy operates at a different layer: you define a program with Python control flow and typed signatures, then the compiler optimizes the prompts against a metric. If you reach for DSPy expecting LangChain-style composition, you are reaching for the wrong tool.

Think of it as PyTorch for LMs — you define the architecture, the compiler tunes the weights (prompts).

Core Paradigm

Read this first. It is the most important thing to understand about DSPy.

import dspy

# 1. Configure the LM
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# 2. Define a signature (input/output schema)
class QASignature(dspy.Signature):
    """Answer questions concisely."""
    question: str = dspy.InputField()
    answer: str = dspy.OutputField()

# 3. Build a program using modules
qa = dspy.ChainOfThought(QASignature)

# 4. Compile against a metric
optimizer = dspy.MIPROv2(metric=dspy.answer_exact_match)
compiled_qa = optimizer.compile(qa, trainset=trainset, num_trials=25)

# 5. Use the compiled program (portable artifact)
answer = compiled_qa(question="What is DSPy?").answer

Core Principles

1. DSPy is a compiler, not a chain framework. You define the program structure with Python control flow and typed signatures. The compiler optimizes the prompts. This is fundamentally different from LangChain's explicit prompt composition.

2. Signatures define the task. Input/output field pairs with optional descriptions are the task definition. The syntax is input1, input2 -> output1, output2.

3. Modules are program components. dspy.Predict (direct), dspy.ChainOfThought (reasoning), dspy.ReAct (tool-use), and custom dspy.Module subclasses. Compose them with Python control flow (if/for/while).

4. Optimizers tune prompts, not weights. A dozen optimizers (teleprompters) tune instructions, few-shot demos, or both. Selection depends on bottleneck and budget. See the optimizer cheat sheet.

5. Compile once, serve many. Compilation is expensive ($3-$300+). The output is a portable artifact via program.save(path). Inference is cheap.

6. Cache aggressively. DSPy caches all LM calls by default. Set DSPY_CACHEDIR for the current client. Disable with dspy.LM(..., cache=False).

Where to Start

You already have...	Start here
Nothing — exploring DSPy	Understand the paradigm (read this page first), then build a simple Predict program
A working prompt you want to optimize	Port to a DSPy Signature, add ChainOfThought, compile with BootstrapFewShot
A multi-step pipeline	Build as a custom dspy.Module with Python control flow, compile with MIPROv2
An agent/tool-use task	Use dspy.ReAct with tools, compile with GEPA or AvatarOptimizer
Comparing frameworks	See the Framework Routing Guide

Quick Reference

Task	Approach	Reference
Basic prediction	dspy.Predict(signature)	references/core-modules.md
With reasoning	dspy.ChainOfThought(signature)	references/core-modules.md
With tools	dspy.ReAct(tools=tools)	references/agent-patterns.md
Custom program	class MyProgram(dspy.Module)	references/program-patterns.md
Quick optimization	dspy.BootstrapFewShot(metric)	references/optimizer-guide.md
Full optimization	dspy.MIPROv2(metric, auto="medium")	references/optimizer-guide.md
Evaluation	dspy.Evaluate(metric=fn, devset=examples)	references/evaluation.md
Save/load	program.save(path) / program.load(path)	references/compilation-guide.md
Retrieval	dspy.Retrieve(k=5)	references/program-patterns.md

Framework Routing Guide

Scenario	Reach for	Why
Prompt optimization / compiled programs	DSPy	Only framework that auto-optimizes prompts against a metric
Documents to query / RAG	LlamaIndex	Data ingestion and retrieval are first-class primitives
Chain/agent composition	LangChain	LCEL is the cleanest pipe-based composition model
State-machine multi-agent	LangGraph	Graph topology, subgraphs, human-in-the-loop
Search pipelines	Haystack	Pipeline model is more mature for search workloads
Role-based teams	CrewAI	Higher-level agent abstraction

Reference Files

Reference	Load when	File
Core Modules	Building with Predict, ChainOfThought, ReAct	references/core-modules.md
Optimizer Guide	Choosing and configuring an optimizer	references/optimizer-guide.md
Program Patterns	RAG, classification, multi-step, tool-use	references/program-patterns.md
Evaluation	Metrics, evaluation loop, dataset creation	references/evaluation.md
Compilation Guide	Caching, cost management, save/load	references/compilation-guide.md
Agent Patterns	ReAct agent, tool-use, AvatarOptimizer	references/agent-patterns.md
FAQ & Troubleshooting	Common errors and fixes	references/faq-and-troubleshooting.md
Validation Audit	Research validation of all API claims	references/validation-audit.md
Worked RAG Example	Full RAG compilation with expected output	references/example-rag-compilation.md

Template Files

Template	When to use	File
Classification	Text classification with BootstrapFewShot	templates/classification.py
RAG Program	RAG with ColBERT retrieval and ChainOfThought	templates/rag-program.py
Multi-Step Reasoning	Multi-step program with tool-use	templates/multi-step.py

Scripts

Script	Purpose	File
check-setup	Verify DSPy installation and configuration	scripts/check-setup.py

Troubleshooting

Symptom	Likely cause	Fix	Reference
Compilation too slow	Too many candidates/threads	Reduce num_candidates or use auto="light"	references/optimizer-guide.md
Compilation too expensive	No caching	Enable DSPY_CACHEDIR	references/compilation-guide.md
Context too long	Too many demos	Reduce max_bootstrapped_demos and max_labeled_demos	references/faq-and-troubleshooting.md
Low quality after compile	Wrong optimizer for bottleneck	Check cheat sheet: instructions vs demos vs weights	references/optimizer-guide.md
Program is not improving	Metric not discriminating	Use a metric that returns float, not bool	references/evaluation.md
Sub-module not updating	_compiled flag set	Set module._compiled = False before recompiling	references/compilation-guide.md

When NOT to Use DSPy

• Simple single-prompt application — raw API calls are simpler
• Need pre-built application modules (PDF Q&A, text-to-SQL) — use LlamaIndex or LangChain
• One-shot task with no optimization budget — DSPy's compiler overhead won't amortize
• Real-time latency-critical — compilation happens at development time but adds no inference overhead