DSPy Fundamentals (3.2.x)

DSPy is the "PyTorch for prompts" — you declare Signatures (typed I/O contracts), compose them into Modules, and let optimizers (not you) tune the instructions and few-shot examples. Never write raw prompts.

The one-paragraph model

Configure a single LM globally with dspy.configure(lm=...). Define a dspy.Signature subclass with dspy.InputField() / dspy.OutputField() (docstring becomes the instruction). Wrap it in a predictor — dspy.Predict (direct), dspy.ChainOfThought (adds reasoning), dspy.ReAct (tool-using agent), dspy.ProgramOfThought (code-executing), or dspy.RLM (long-context). Subclass dspy.Module to compose multi-step programs. For built-in providers, use dspy.LM("provider/model"); for a truly custom backend, subclass dspy.BaseLM. Optimize later with GEPA.

Canonical template

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o"), track_usage=True)

class QuestionAnswer(dspy.Signature):
    """Answer questions with rigorous step-by-step reasoning."""
    question: str = dspy.InputField()
    answer: str = dspy.OutputField(desc="concise final answer")

class QAProgram(dspy.Module):
    def __init__(self):
        super().__init__()
        self.solve = dspy.ChainOfThought(QuestionAnswer)

    def forward(self, question: str) -> dspy.Prediction:
        return self.solve(question=question)

program = QAProgram()
pred = program(question="What is 2 + 2?")
print(pred.reasoning, pred.answer)

Predictor cheatsheet (DSPy 3.2.x)

Predictor	When to use	Adds
dspy.Predict(sig)	Simple structured I/O	nothing — just the signature
dspy.ChainOfThought(sig)	Reasoning tasks	a reasoning output field
dspy.ReAct(sig, tools=[...], max_iters=20)	Tool-using agent	Thought/Action/Observation loop
dspy.ProgramOfThought(sig, max_iters=3)	Math/data tasks	generates & runs Python (needs Deno)
dspy.RLM(sig, ...)	Long context / codebases	recursive REPL exploration (see dspy-rlm-module)

Typed outputs — use Pydantic on fields, not TypedPredictor

dspy.TypedPredictor is superseded; dspy.Predict now handles Pydantic types natively via field annotations.

from pydantic import BaseModel
from typing import Literal

class Entity(BaseModel):
    name: str
    kind: Literal["person", "org", "place"]

class ExtractEntities(dspy.Signature):
    """Extract named entities from text."""
    text: str = dspy.InputField()
    entities: list[Entity] = dspy.OutputField()

extractor = dspy.Predict(ExtractEntities)

Save & load

Two modes — know the difference:

# State-only (portable JSON; you must rebuild the architecture to load)
program.save("program.json", save_program=False)
new = QAProgram(); new.load("program.json")

# Full program (cloudpickle into a directory; restores everything)
program.save("./program_dir/", save_program=True)
restored = dspy.load("./program_dir/")

Prefer state-only for version control; full-program for deployment artifacts.

Ten anti-patterns to refuse

1. Hard-coded prompt strings ("You are a helpful assistant...") — write a Signature.
2. dspy.TypedPredictor(...) in new code — use dspy.Predict with Pydantic fields.
3. dspy.OpenAI(...) / dspy.settings.configure(...) — use dspy.configure(lm=dspy.LM(...)).
4. Provider-specific LM classes for built-in providers — use dspy.LM("provider/model"). If DSPy doesn't ship your backend, subclass dspy.BaseLM.
5. Giant monolithic predictors that do five jobs — decompose into a Module with named sub-predictors.
6. Mutating signature.instructions by hand — let the optimizer do it.
7. In-lining few-shot demos in the Signature docstring — bootstrap/optimize them.
8. Using pickle.dump(program) — use program.save(...).
9. Setting an LM per module at construction time without reason — configure globally, override only when you need model mixing.
10. Vague metrics (yes/no, exact-match only) when training an optimizer — see dspy-evaluation-harness.

Configuring the LM

dspy.configure(
    lm=dspy.LM("openai/gpt-4o", temperature=0.0, max_tokens=2000),
    track_usage=True,        # accumulate token counts on predictions
    async_max_workers=4,     # for .acall / batch
)

DSPy 3.2.x warns by default when a module call passes extra input fields or values that don't match the signature's declared types. Treat those warnings as a callsite bug first; if you're intentionally passing pre-serialized values, disable them with dspy.configure(warn_on_type_mismatch=False).

Common provider prefixes: openai/, anthropic/, azure/, vertex_ai/, bedrock/, ollama/. For local Ollama: dspy.LM("ollama_chat/llama3.1:8b", api_base="http://localhost:11434").

Where to go next

• Measuring quality → dspy-evaluation-harness
• Automatic optimization → dspy-gepa-optimizer
• Context >100k tokens → dspy-rlm-module
• Full pipeline → dspy-advanced-workflow
• Full API reference → reference.md
• Runnable example → example_qa.py