lebsral/dspy-predict

Direct LM Calls with dspy.Predict

Guide the user through using dspy.Predict -- the simplest and fastest DSPy module for calling a language model. It takes inputs, calls the LM, and returns typed outputs. No intermediate reasoning, no extra steps.

What is dspy.Predict

dspy.Predict is the atomic building block of every DSPy program -- one LM call, no reasoning chain, no tool loops. It takes a signature and calls the LM once to produce the output fields. Every other DSPy module (ChainOfThought, ReAct, etc.) builds on top of it.

When to use Predict vs ChainOfThought

| Use dspy.Predict when... | Use dspy.ChainOfThought when... | |---|---| | The task is straightforward (classification, extraction, formatting) | The task benefits from step-by-step reasoning | | You want minimal latency and token usage | Accuracy matters more than speed | | The mapping from input to output is direct | The LM needs to "think through" intermediate steps | | You're building a simple sub-step inside a larger pipeline | You need to inspect the model's reasoning |

Rule of thumb: Start with Predict. If accuracy is too low, switch to ChainOfThought -- it's a one-word change.

Predict with Pydantic output types

For complex structured outputs, use a Pydantic BaseModel as the output type:

import dspy
from pydantic import BaseModel

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

class ContactInfo(BaseModel):
    name: str
    email: str
    phone: str

class ExtractContact(dspy.Signature):
    """Extract contact information from the text."""
    text: str = dspy.InputField()
    contact: ContactInfo = dspy.OutputField()

extractor = dspy.Predict(ExtractContact)
result = extractor(text="Reach out to Jane Doe at [email protected] or 555-0123")
print(result.contact.name)   # Jane Doe
print(result.contact.email)  # [email protected]
print(result.contact.phone)  # 555-0123

Async and batch processing

For async calls, use acall or aforward:

result = await predict.acall(question="What is DSPy?")

For batch processing, use the built-in batch() method instead of a Python loop:

examples = [dspy.Example(question=q).with_inputs("question") for q in questions]
results = predict.batch(examples, num_threads=8, timeout=120)

Save and load optimized predictors:

predict.save("my_predictor.json")
predict.load("my_predictor.json")

Gotchas

1. Predict is not "dumb" -- optimizers can add few-shot demos and tuned instructions, making Predict surprisingly powerful. Don't underestimate it.
2. If accuracy is low, try ChainOfThought before reaching for complex solutions -- it's a one-word swap (dspy.ChainOfThought instead of dspy.Predict) and often gets you 10-20% accuracy gains on reasoning-heavy tasks.
3. For batch processing, use predict.batch() rather than a Python loop -- it uses dspy.Parallel internally, handles concurrency, and is significantly faster for large batches.
4. Only keyword arguments -- predict("my input") raises ValueError. Always use predict(question="my input").

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Defining signatures (inline and class-based, typed fields, Pydantic outputs) -- see /dspy-signatures
• Adding step-by-step reasoning -- see /dspy-chain-of-thought
• Building multi-step programs with modules that compose Predict calls -- see /dspy-modules
• Classification and sorting with real-world patterns -- see /ai-sorting
• For worked examples, see examples.md
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do

Additional resources

• dspy.Predict API docs
• For complete constructor signatures and method reference, see reference.md
• For worked examples (classification, extraction, batch processing), see examples.md