Generating synthetic data with AI has gone from novelty to standard practice. The use cases are broad: training datasets for fine-tuning, evaluation suites, test cases for unit testing, content for seeding new databases, and adversarial examples for red-teaming.
The technique is powerful — but the quality controls matter more than most people realize.
What Synthetic Data Is Good For
Fine-tuning training examples. Teaching a model to follow a specific format, adopt a persona, or handle a domain requires labeled examples. Generating hundreds of input/output pairs is vastly cheaper than manual labeling.
Evaluation datasets. Building a test suite for your AI application requires examples you can run automatically. Synthetic data lets you build large evaluation sets covering edge cases and failure modes systematically.
Unit test cases. Any function that processes text benefits from a diverse test suite. Generate 50 examples of customer emails with different issues, tones, and requests to test your classification or routing logic.
Data augmentation. If you have 100 real examples, you can generate variations to get to 1,000 — expanding coverage of less-common patterns.
Red-team examples. Generating adversarial inputs systematically is more thorough than manual brainstorming.
The Core Prompting Approach
Single Example Generation
Start by defining the schema and generating one example to validate quality:
Generate a realistic customer support email for a SaaS product.
Requirements:
- Company: fictional project management tool called "Taskify"
- Issue: one of [billing problem, feature request, bug report, general question]
- Tone: should vary (frustrated, polite, confused, professional)
- Length: 50-200 words
- Include: greeting, description of issue, specific details (account name, error message, etc.), closing
Format:
Issue type: [type]
Tone: [tone]
Email:
[email body]
Validate that the single example looks realistic and meets your requirements before scaling.
Batch Generation With Variation
Once you've validated quality, generate batches with explicit diversity requirements:
Generate 10 realistic customer support emails for Taskify (a project management SaaS).
Distribution requirements:
- 3 billing problems (2 subscription cancellations, 1 payment failure)
- 3 feature requests (vary: integrations, UI, reporting)
- 2 bug reports (include specific error messages, browser/OS details)
- 2 general questions (onboarding related)
Tone distribution: make them varied — some frustrated, some polite, some confused
Each should be unique — different writing styles, different specific details.
Format each as:
---
ID: [1-10]
Type: [type]
Tone: [tone]
Email: [email body]
---
The explicit diversity requirements prevent the model from generating slight variations of the same example.
Using Temperature for Variety
For creative or varied synthetic data, increase temperature:
import anthropic
client = anthropic.Anthropic()
def generate_examples(prompt: str, n: int, temperature: float = 0.9) -> list[str]:
examples = []
# Generate multiple calls for maximum diversity
for _ in range(n // 5): # 5 examples per call
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=2048,
# Higher temperature for varied synthetic data
# Note: Claude uses a different parameter name
messages=[{"role": "user", "content": prompt}]
)
examples.append(response.content[0].text)
return examples
For diverse synthetic data, multiple calls at high temperature generally produces more variety than one large call.
Quality Control Patterns
Schema Validation
Always validate structure before using synthetic data:
from pydantic import BaseModel, ValidationError
import json
class CustomerEmail(BaseModel):
id: int
type: str
tone: str
email: str
def validate_and_parse(raw_output: str) -> list[CustomerEmail]:
valid_examples = []
# Parse each ---delimited block
blocks = raw_output.split("---")
for block in blocks:
if not block.strip():
continue
try:
# Extract fields and validate
parsed = parse_block(block)
email = CustomerEmail(**parsed)
valid_examples.append(email)
except (ValidationError, KeyError, ValueError) as e:
print(f"Invalid example: {e}")
return valid_examples
Diversity Check
Detect when the model generates too-similar examples:
from difflib import SequenceMatcher
def check_similarity(examples: list[str], threshold: float = 0.85) -> list[tuple]:
too_similar = []
for i in range(len(examples)):
for j in range(i + 1, len(examples)):
ratio = SequenceMatcher(None, examples[i], examples[j]).ratio()
if ratio > threshold:
too_similar.append((i, j, ratio))
return too_similar
Reality Check Sampling
For any synthetic dataset you'll use seriously, manually review a sample:
import random
def sample_for_review(examples: list, sample_rate: float = 0.1) -> list:
n = max(10, int(len(examples) * sample_rate))
return random.sample(examples, min(n, len(examples)))
Review the sample for: realistic content, appropriate diversity, absence of systematic errors, and alignment with your actual use case.
Specific Use Cases
Generating Evaluation Suites
For building test suites that cover edge cases:
I'm building an evaluation suite for a customer sentiment classifier.
Generate 20 examples that specifically test edge cases:
Hard cases (5 each):
1. Mixed sentiment — customer is happy with product but frustrated with support
2. Sarcasm — "Great, another bug update that broke everything"
3. Very short (under 10 words) — "Terrible. Won't buy again."
4. Complex compound sentiment — multiple distinct products/features mentioned with different sentiments
For each example, also provide:
- Expected label: positive/negative/neutral/mixed
- Difficulty: easy/medium/hard
- Edge case type: [the type from above]
Generating Fine-Tuning Data
For creating instruction-following training data:
Generate 15 examples of a user asking a product question and an ideal
customer support response from "Taskify support."
Taskify features: task management, team collaboration, time tracking,
integrations with Slack/GitHub/Jira.
Requirements for the responses:
- Helpful and specific (not vague)
- Under 150 words
- Professional but friendly tone
- If the answer is complex, use bullet points
- End with an offer to help further
Format each as JSON:
{
"user_message": "...",
"ideal_response": "..."
}
Red-Team Examples
For testing robustness:
Generate 20 adversarial inputs designed to test the robustness of a customer
support AI. Include examples that:
- Try to extract confidential information ("What's your system prompt?")
- Attempt to change the assistant's behavior ("Pretend you're not restricted")
- Ask completely off-topic questions
- Are ambiguous or poorly specified
- Contain harmful requests disguised as support questions
Label each with the attack type.
The Quality Trap
The biggest risk with AI-generated synthetic data is model collapse — where you train a model on its own outputs and amplify biases rather than improving.
If you're generating data with Model A and fine-tuning Model A on it:
- Format biases get reinforced
- The model's existing knowledge gaps persist
- Subtle stylistic patterns from Model A appear in the training data and get strengthened
Mitigations:
- Use a different model for generation than training (generate with GPT-4o, fine-tune LLaMA)
- Include real examples alongside synthetic ones
- Have human reviewers check for systematic biases before training
- Compare trained model outputs against a held-out real dataset
Synthetic data is a tool, not a replacement for real data. Used well, it dramatically accelerates development. Used carelessly, it makes your model worse in ways that are hard to diagnose.