Generate Synthetic Training Data

Guide the user through generating high-quality synthetic training data with DSPy. This solves the “I don’t have data” problem that blocks every other AI workflow.

When you need synthetic data

• Cold start: You’re building a new feature and have zero labeled examples
• Not enough for optimization: You have 10-30 examples but optimizers need 200+
• Privacy/compliance: You can’t use real customer data for training
• Edge cases: Your AI works on common inputs but fails on rare ones
• Unbalanced categories: Some categories have 500 examples, others have 10
• New categories: You added a category and have no examples for it
• Schema changed: Your input/output format changed, old data doesn’t fit
• Proof of concept: PM wants a demo by Friday, no time to collect real data

The core idea

Define a generator signature whose outputs match your task’s input/output fields. Use an LM to produce examples. Filter for quality. Use for optimization.

Research shows this works surprisingly well:

• Optimized generator prompts match models trained on 100K+ human labels using only 10 gold labels (arXiv 2406.11706)
• DSPy-optimized Chain-of-Thought generation outperforms hand-written static templates (arXiv 2508.13930)

The key insight: the prompt used to generate data is a critical hyperparameter — optimizing it matters more than generating more data.

Step 1: Define what an example looks like

Your generator’s outputs should match your task’s inputs and expected outputs.

import dspy

# Your task — what the AI will do in production
class ClassifyTicket(dspy.Signature):
    """Classify a support ticket into a category."""
    ticket_text: str = dspy.InputField()
    category: str = dspy.OutputField()

# Generator — produces examples for your task
class GenerateTicketExample(dspy.Signature):
    """Generate a realistic support ticket with its correct category."""
    category: str = dspy.InputField(desc="the target category to generate an example for")
    ticket_text: str = dspy.OutputField(desc="a realistic support ticket for this category")

The generator’s output fields become inputs to your task. Think of it as: “given what I want the answer to be, generate a realistic input.”

Multi-field tasks

If your task has multiple inputs or outputs, mirror all of them:

# Task: extract structured data from text
class ExtractContact(dspy.Signature):
    """Extract contact info from a message."""
    message: str = dspy.InputField()
    name: str = dspy.OutputField()
    email: str = dspy.OutputField()
    phone: str = dspy.OutputField()

# Generator: produce realistic messages with known contact info
class GenerateContactExample(dspy.Signature):
    """Generate a realistic message that contains contact information."""
    name: str = dspy.InputField(desc="the person's name to embed in the message")
    email: str = dspy.InputField(desc="the email address to embed in the message")
    phone: str = dspy.InputField(desc="the phone number to embed in the message")
    message: str = dspy.OutputField(desc="a realistic message containing this contact info")

Step 2: Write seed examples

Start with 5-10 hand-written examples. These anchor the generator’s understanding of what “realistic” means for your domain.

seeds = [
    dspy.Example(
        ticket_text="I was charged twice for my subscription this month. Order #4521.",
        category="billing"
    ).with_inputs("ticket_text"),
    dspy.Example(
        ticket_text="The app crashes when I try to upload a profile photo on Android.",
        category="bug"
    ).with_inputs("ticket_text"),
    dspy.Example(
        ticket_text="How do I export my data to CSV? I can't find the option anywhere.",
        category="how-to"
    ).with_inputs("ticket_text"),
    dspy.Example(
        ticket_text="I'd love to see dark mode added. The white background hurts my eyes.",
        category="feature-request"
    ).with_inputs("ticket_text"),
    dspy.Example(
        ticket_text="My account got locked after too many login attempts. Please help.",
        category="account"
    ).with_inputs("ticket_text"),
]

Even 5 seeds dramatically improve generation quality over zero.

Step 3: Generate in batches

Two patterns depending on your LM provider:

Pattern A: n=N batch generation

When your provider supports the n parameter (OpenAI does), this generates multiple completions in one call — faster and often more diverse:

generator = dspy.Predict(GenerateTicketExample, n=20)
response = generator(category="billing")
examples = [
    dspy.Example(ticket_text=t, category="billing").with_inputs("ticket_text")
    for t in response.completions.ticket_text
]

Pattern B: Loop generation

Works with any provider. More control over each example:

examples = []
categories = ["billing", "bug", "how-to", "feature-request", "account"]

for category in categories:
    generator = dspy.Predict(GenerateTicketExample)
    for i in range(40):
        result = generator(category=category)
        examples.append(
            dspy.Example(ticket_text=result.ticket_text, category=category)
            .with_inputs("ticket_text")
        )

print(f"Generated {len(examples)} examples")

The n parameter isn’t supported by all providers — use the loop pattern as a reliable fallback.

Generation strategies

Pick the strategy that fits your gap:

Category-driven — generate N per category (fixes imbalance):

for category in categories:
    for i in range(50):
        result = generator(category=category)
        examples.append(dspy.Example(ticket_text=result.ticket_text, category=category).with_inputs("ticket_text"))

Seed-and-vary — pass a seed example with a variation instruction:

class GenerateVariation(dspy.Signature):
    """Generate a variation of this support ticket with a different tone and phrasing."""
    original_ticket: str = dspy.InputField(desc="the original ticket to vary")
    variation_type: str = dspy.InputField(desc="how to vary it: tone, length, complexity, or language")
    ticket_text: str = dspy.OutputField(desc="a new ticket with the same meaning but different style")

vary = dspy.Predict(GenerateVariation)
for seed in seeds:
    for variation in ["angry tone", "very brief", "verbose and detailed", "non-native English"]:
        result = vary(original_ticket=seed.ticket_text, variation_type=variation)
        examples.append(dspy.Example(ticket_text=result.ticket_text, category=seed.category).with_inputs("ticket_text"))

Scenario-driven — specify edge case scenarios:

class GenerateScenarioTicket(dspy.Signature):
    """Generate a support ticket matching a specific scenario."""
    category: str = dspy.InputField()
    scenario: str = dspy.InputField(desc="the specific scenario to generate")
    ticket_text: str = dspy.OutputField()

gen = dspy.Predict(GenerateScenarioTicket)
scenarios = [
    ("billing", "customer charged in wrong currency"),
    ("billing", "refund for a cancelled subscription"),
    ("bug", "issue only happens on slow network connections"),
    ("bug", "multi-step reproduction involving two features"),
    ("how-to", "customer is non-technical and confused by jargon"),
]
for category, scenario in scenarios:
    result = gen(category=category, scenario=scenario)
    examples.append(dspy.Example(ticket_text=result.ticket_text, category=category).with_inputs("ticket_text"))

Difficulty-driven — generate easy, medium, hard examples separately:

class GenerateByDifficulty(dspy.Signature):
    """Generate a support ticket at a specific difficulty level for classification."""
    category: str = dspy.InputField()
    difficulty: str = dspy.InputField(desc="easy (clear-cut), medium (some ambiguity), or hard (could be multiple categories)")
    ticket_text: str = dspy.OutputField()

gen = dspy.Predict(GenerateByDifficulty)
for category in categories:
    for difficulty in ["easy", "medium", "hard"]:
        for i in range(15):
            result = gen(category=category, difficulty=difficulty)
            examples.append(dspy.Example(ticket_text=result.ticket_text, category=category).with_inputs("ticket_text"))

Diversity trick — add a random sindex field to push the LM toward varied outputs:

import random

class GenerateDiverse(dspy.Signature):
    """Generate a unique and realistic support ticket."""
    category: str = dspy.InputField()
    sindex: str = dspy.InputField(desc="a unique seed index for diversity")
    ticket_text: str = dspy.OutputField()

gen = dspy.Predict(GenerateDiverse)
for category in categories:
    for i in range(50):
        result = gen(category=category, sindex=str(random.randint(0, 1_000_000)))
        examples.append(dspy.Example(ticket_text=result.ticket_text, category=category).with_inputs("ticket_text"))

The random sindex prevents the LM from falling into repetitive patterns.

Step 4: Filter for quality

Generated data always contains some bad examples. Filter aggressively — aim to generate 2-3x what you need and keep ~50%.

Simple: metric-based filtering

Run each generated example through your task program and check with your metric:

program = dspy.ChainOfThought(ClassifyTicket)
filtered = []

for ex in examples:
    pred = program(**ex.inputs())
    if metric(ex, pred):
        filtered.append(ex)

print(f"Kept {len(filtered)}/{len(examples)} ({100*len(filtered)//len(examples)}%)")

This works when your program is already decent — it filters out examples that are confusing or mislabeled.

Robust: LM-based assessment

Use a separate assessment step to check realism and correctness:

class AssessExample(dspy.Signature):
    """Is this a realistic and correctly labeled example?"""
    ticket_text: str = dspy.InputField()
    category: str = dspy.InputField()
    is_realistic: bool = dspy.OutputField(desc="true if this looks like a real support ticket")
    is_correctly_labeled: bool = dspy.OutputField(desc="true if the category matches the ticket")

assessor = dspy.Predict(AssessExample)
filtered = []

for ex in examples:
    result = assessor(ticket_text=ex.ticket_text, category=ex.category)
    if result.is_realistic and result.is_correctly_labeled:
        filtered.append(ex)

print(f"Kept {len(filtered)}/{len(examples)} ({100*len(filtered)//len(examples)}%)")

Quality gates with dspy.Suggest

For tighter integration, build quality checks into the generator itself. When a Suggest constraint fails, DSPy retries the generation:

class QualityGenerator(dspy.Module):
    def __init__(self):
        self.generate = dspy.ChainOfThought(GenerateTicketExample)
        self.assess = dspy.Predict(AssessExample)

    def forward(self, category):
        result = self.generate(category=category)
        assessment = self.assess(ticket_text=result.ticket_text, category=category)
        dspy.Suggest(assessment.is_realistic, "Generated ticket should be realistic")
        dspy.Suggest(assessment.is_correctly_labeled, "Category label should be correct")
        return result

generator = QualityGenerator()
# DSPy retries generation when Suggest constraints fail

Check for duplicates

Remove near-duplicates to keep your dataset diverse:

seen = set()
unique = []
for ex in filtered:
    # Normalize and check
    key = ex.ticket_text.strip().lower()
    if key not in seen:
        seen.add(key)
        unique.append(ex)

print(f"Removed {len(filtered) - len(unique)} near-duplicates")
filtered = unique

Step 5: Optimize the generator itself (advanced)

Research (arXiv 2406.11706) shows that optimizing the prompt used to generate data dramatically improves downstream quality. This is meta-optimization: optimizing the generator so it produces better training data.

class DataGenerator(dspy.Module):
    def __init__(self):
        self.generate = dspy.ChainOfThought(GenerateTicketExample)

    def forward(self, category):
        return self.generate(category=category)

# Define a metric that measures generated data quality
def generator_metric(example, prediction, trace=None):
    # Check if a downstream classifier gets the right answer on this generated example
    classifier = dspy.Predict(ClassifyTicket)
    task_example = dspy.Example(ticket_text=prediction.ticket_text, category=example.category).with_inputs("ticket_text")
    task_pred = classifier(**task_example.inputs())
    return task_pred.category.lower() == example.category.lower()

# Optimize the generator's prompts
optimizer = dspy.BootstrapFewShot(metric=generator_metric)
optimized_generator = optimizer.compile(DataGenerator(), trainset=seeds)

# Now generate with the optimized generator
better_examples = []
for category in categories:
    for i in range(50):
        result = optimized_generator(category=category)
        better_examples.append(
            dspy.Example(ticket_text=result.ticket_text, category=category).with_inputs("ticket_text")
        )

This closes the loop: better generator prompts produce better data, which produces better task programs.

Step 6: Use generated data for optimization

Full pipeline: generate, filter, split, optimize, evaluate.

import random
from dspy.evaluate import Evaluate

# Shuffle and split
random.shuffle(filtered)
split = int(len(filtered) * 0.8)
trainset = filtered[:split]
devset = filtered[split:]

print(f"Train: {len(trainset)}, Dev: {len(devset)}")

# Configure your task LM (can be cheaper than the generator LM)
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# Build and optimize your task program
program = dspy.ChainOfThought(ClassifyTicket)

optimizer = dspy.MIPROv2(metric=metric, auto="medium")
optimized = optimizer.compile(program, trainset=trainset)

# Evaluate
evaluator = Evaluate(devset=devset, metric=metric, num_threads=4, display_progress=True)
score = evaluator(optimized)
print(f"Score on synthetic dev set: {score:.1f}%")

# Save
optimized.save("optimized_program.json")

If you have even a small number of real examples, use them as the dev set instead — real data gives more trustworthy evaluation.

Common scenarios

Cold start — zero real data. Write 5-10 seeds. Generate 200+ synthetic examples across all categories. Filter and optimize. See examples.md for a full walkthrough.

Edge case gaps — your AI works at 85% but fails on specific scenarios. Run error analysis, identify the failure patterns, then use scenario-driven generation targeting those gaps. Re-optimize with the augmented dataset.

Privacy/compliance — can’t use real customer data. Generate synthetic examples with realistic patterns but no PII. Validate with domain-specific assessments. The dspy.Suggest quality gate pattern ensures generated data meets your standards.

New categories — added a category with no examples. Use category-driven generation to produce 50+ examples for the new category, then retrain.

Rebalancing — some categories have 500 examples, others have 10. Generate more for underrepresented categories until all are roughly balanced.

Schema changed — your input/output format changed. Generate new examples matching the new schema rather than manually converting old data.

Tips and pitfalls

• Always validate generated data — LMs produce plausible but wrong labels. Filter aggressively.
• Mix synthetic with real data when available — even 20 real examples mixed in improve quality significantly.
• Use a stronger model to generate, cheaper model for your task — e.g., generate with GPT-4o, run your task on GPT-4o-mini.
• Generate more than you need — aim for 2-3x your target, keep ~50% after filtering.
• Check for duplicates — LMs tend to repeat themselves, especially without the diversity trick.
• Iterate — generate, optimize, evaluate, identify gaps, generate more for gaps.
• Don’t trust synthetic eval scores blindly — if possible, validate final quality on real data.
• The n parameter for batch generation isn’t supported by all providers — use the loop pattern as a reliable fallback.

Additional resources

• For end-to-end worked examples (cold start, edge cases, privacy), see examples.md
• Use /ai-improving-accuracy to measure and improve your optimized program
• Use /ai-fine-tuning once you have enough generated data for weight optimization
• Use /ai-kickoff to scaffold a project, then fill data with this skill