Bootstrapping AI Systems with Synthetic Data: 4 Approaches

ai
flywheel
synthetic-data
Published

March 23, 2025

Your CEO is asking why the AI isn’t working, but you’re caught in the classic catch-22: you need user data to improve the AI, but you need working AI to attract users.

Without evaluation data, you’re essentially guessing whether your AI works. You’re not alone.

The solution lies in synthetic data generation for AI evaluation—creating artificial yet realistic data to build systematic evaluation frameworks before real users arrive. This isn’t just about training models; it’s about building the evaluation systems that let you stop guessing and start knowing whether your AI actually works.

As someone who’s taken multiple data products from inception to international launch, I’ve discovered that synthetic data for evaluation isn’t just a stopgap measure—it’s often the critical leverage point that transforms AI development from “vibes-based” to systematic.

Why Synthetic Data Works for AI Evaluation

Synthetic data refers to information created via simulation or generative models rather than collected from real-world events. For AI evaluation, it offers compelling advantages that transform how engineering teams build confidence in their systems:

  • Speed: Generate evaluation datasets quickly instead of waiting months for user feedback
  • Coverage: Create test cases for edge cases and failure modes that might take years to encounter naturally
  • Systematic Testing: Build comprehensive evaluation suites that catch problems before users do
  • Confidence Building: Move from “I think this works” to “I know this works based on systematic evaluation”

The key insight is that synthetic data for evaluation doesn’t need to be perfect—it needs to be systematically comprehensive enough to catch real problems before they reach users. This is the foundation of evaluation-driven development that transforms AI from a “black box” into a predictable system.

Let me share proven techniques I’ve used to build systematic evaluation frameworks using synthetic data—the foundation that moves teams from flying blind to confident control.

1. Generating Q&A Pairs for Systematic Evaluation

One of the most effective strategies for building systematic evaluation of Q&A systems is to generate realistic test cases from your existing documentation. This isn’t just about training data—it’s about building the evaluation framework that lets you measure and improve performance systematically.

The technique:

  1. Break your documentation into logical chunks
  2. For each chunk, prompt a large language model to generate questions that could be answered by that content
  3. Ensure questions are natural, self-contained, and vary in complexity

For example, if your documentation states: “The AI system can schedule meetings automatically across time zones,” you might generate questions like: - “How does the scheduling system handle time zone differences?” - “Can I schedule meetings with participants in different countries?” - “Does the system automatically adjust for daylight saving changes?”

class QA(BaseModel):
    question: str
    answer: str

@llm.call(provider="openai", model="gpt-4o-mini", response_model=QA)
@prompt_template("""
Generate a realistic user question and a correct answer based on the documentation below.

Documentation:
{doc}

Provide the question first, then the answer.
""")
def generate_qa(doc: str): ...

doc_snippet = "The AI system can schedule meetings automatically across time zones."
qa_pair = generate_qa(doc_snippet)

This creates a foundational evaluation dataset that covers your knowledge base comprehensively—transforming “I think this works” into “I know this works based on systematic testing.”

2. Building Systematic Robustness Testing

A sign of systematic AI evaluation is testing how gracefully your system handles questions it cannot answer. Instead of discovering these gaps through user complaints, generate synthetic unanswerable queries for systematic testing.

The technique:

  1. Create questions that sound plausible but fall outside your system’s knowledge
  2. Use the “No Overlap” strategy: deliberately avoid keywords from the context
  3. Generate adversarial or trick questions with impossible premises

For example, if your system knows about scheduling meetings, an unanswerable question might be: “Can the AI system translate meetings into Spanish in real-time?”

This is evaluation-driven development in action: systematically testing failure modes before users encounter them. Users actually trust systems more when they confidently admit what they don’t know, rather than attempting to answer everything.

@llm.call(provider="openai", model="gpt-4o-mini")
@prompt_template("""
Given the documentation below, generate a plausible user question **not** answered by that documentation.

Documentation:
{doc}
""")
def generate_unanswerable(doc: str) -> str: ...

print(generate_unanswerable(doc_snippet))

3. Systematic Testing Across User Diversity

Real users will ask the same questions in countless different ways. Instead of discovering this through user churn, build systematic evaluation by generating paraphrases across different user dimensions.

The technique:

  1. Define user dimensions: mood (curious, frustrated), proficiency level, age group, language fluency
  2. Generate query variants for each dimension
  3. Use back-translation (translate to another language and back) for additional variety

For instance, “How do I reset my password?” could become: - Frustrated: “Why can’t I figure out how to reset this stupid password?” - Non-native speaker: “How to making reset of my password, please?” - Technical user: “What’s the procedure to initiate a password reset on my account?”

This systematic approach multiplies your evaluation coverage without changing the underlying intent, building confidence that your model handles the linguistic diversity of real users—before they encounter failures.

4. Building Systematic Evaluation for Recommendation Systems

When I led recommendation projects at LinkedIn, we often had to launch new features with zero interaction data. The key wasn’t just generating training data—it was building systematic evaluation frameworks that gave us confidence before real users arrived.

The technique:

  1. Create synthetic user profiles with interest distributions
  2. Generate plausible interaction logs based on those profiles
  3. Encode known patterns (like popularity bias) into synthetic interactions

For example, for a job recommendation system, we might create: - A “software engineer” persona who interacts with programming-related content - A “sales professional” who engages with business development materials - A “power user” with hundreds of interactions to test scaling

class UserProfile(BaseModel):
    user_id: str
    persona: str
    interests: Dict[str, float]  # category -> interest level (0-1)
    interaction_frequency: str   # "low", "medium", "high"

class Interaction(BaseModel):
    user_id: str
    item_id: str
    action: str  # "view", "click", "save", etc.
    timestamp: str

class InteractionBatch(BaseModel):
    interactions: List[Interaction]

# Step 1: Generate synthetic user profiles
@llm.call(provider="openai", model="gpt-4o-mini", response_model=UserProfile)
@prompt_template("""
Create a synthetic user profile for a recommendation system with the following details:
- User type: {user_type}
- Create a distribution of interests across these categories: {categories}
- Assign each category an interest score between 0-1 (1 being highest interest)
- Determine if this user is a low, medium, or high frequency user

Format the output as a structured profile.
""")
def generate_user_profile(user_type: str, categories: List[str]): ...

# Step 2: Generate synthetic interactions based on user profiles
@llm.call(provider=model="gpt-4o-mini", response_model=InteractionBatch)
@prompt_template("""
Generate {count} realistic user interactions for a recommendation system based on this user profile:
- User ID: {user_id}
- User Persona: {persona}
- Interest distribution: {interests}
- Interaction frequency: {frequency}

Available items to interact with: {items}
Possible actions: view, click, save, apply

The user should interact more with items that match their interest distribution.
Include timestamps over the past week.
""")
def generate_interactions(
    user_id: str, 
    persona: str, 
    interests: Dict[str, float], 
    frequency: str,
    items: Dict[str, str],  # item_id -> category
    count: int
): ...

# Example usage
categories = ["Software Engineering", "Data Science", "Sales", "Marketing", "Finance"]
items = {
    "job_001": "Software Engineering",
    "job_002": "Software Engineering",
    "job_003": "Data Science",
    "job_004": "Data Science",
    "job_005": "Sales",
    "job_006": "Marketing",
    "job_007": "Finance"
}

# Create a few synthetic users
user_types = ["software engineer", "sales professional", "data scientist"]
synthetic_users = []

for i, user_type in enumerate(user_types):
    user_profile = generate_user_profile(user_type, categories)
    user_profile.user_id = f"user_{i+1}"
    synthetic_users.append(user_profile)

# Generate interactions for each user
all_interactions = []
for user in synthetic_users:
    # Number of interactions depends on frequency
    count = {"low": 5, "medium": 15, "high": 30}[user.interaction_frequency]
    
    batch = generate_interactions(
        user.user_id, 
        user.persona, 
        user.interests,
        user.interaction_frequency,
        items,
        count
    )
    all_interactions.extend(batch.interactions)

# Convert to DataFrame for use in recommendation algorithms
interactions_df = pd.DataFrame([i.__dict__ for i in all_interactions])

With this systematic evaluation framework, you can: 1. Test your recommendation algorithms end-to-end with confidence 2. Ensure your system handles different user behaviors systematically 3. Evaluate recommendation quality metrics before launch 4. Identify performance bottlenecks through systematic testing

This is evaluation-driven development: building confidence through systematic testing rather than flying blind until users complain. When we finally launched, we already had systematic evidence that the infrastructure could handle real data patterns.

Best Practices for Evaluation-Driven Synthetic Data

Through implementing these techniques across multiple organizations, I’ve developed these guiding principles for building systematic evaluation:

  1. Coverage over volume: Comprehensive test cases beat masses of redundant examples
  2. Encode failure modes: Your synthetic data should systematically test known failure patterns
  3. Design for systematic testing: Deliberately include edge cases and failure scenarios
  4. Iterate with real data: As real data arrives, compare with synthetic evaluation to validate your testing framework
  5. Balance realism with systematic coverage: Synthetic data should be realistic enough to catch real problems but comprehensive enough to test systematically

The Transition Strategy: From Evaluation Framework to Confident Deployment

Synthetic data for evaluation is not the end goal—it’s the systematic foundation that builds confidence before real users arrive. The optimal approach follows this progression:

  1. Bootstrap phase: Build comprehensive evaluation framework with synthetic data
  2. Validation phase: As real data arrives, validate that your evaluation framework catches real problems
  3. Continuous evaluation: Use synthetic data to systematically test new features before deployment
  4. Systematic improvement: Use evaluation results to guide development priorities systematically

At HealthRhythms, we followed this exact pattern. By the time we had real user data, we had already built systematic evaluation that caught edge cases, optimized our algorithms, and validated our entire pipeline—transforming development from guesswork to systematic confidence.

Putting It Into Practice: Building Your Evaluation Framework

To get started with systematic AI evaluation using synthetic data:

  1. Identify your evaluation gaps—what failure modes are you flying blind to?
  2. Select the appropriate generation technique for systematic testing
  3. Start small and validate that your evaluation catches real problems
  4. Build evaluation metrics that connect to business outcomes
  5. Implement systematic evaluation as part of your development process

The investment in systematic evaluation pays dividends through confident deployments, fewer user-reported failures, and predictable AI performance.

Building systematic AI evaluation with synthetic data isn’t just a theoretical concept—it’s the practical foundation that transforms teams from flying blind to confident control. By applying these techniques, you can stop guessing whether your AI works and start knowing through systematic evaluation.

Ready to stop flying blind with your AI? If you want to build systematic evaluation that transforms guesswork into confidence, I can help your team implement this in just one week.

Book a free consult to discuss how systematic evaluation can transform your AI development process.