Synthetic vs. Organic Data

Understanding the difference between synthetic and organic data in AI training

What is Synthetic Data?

Synthetic data is artificially generated data created by AI models or algorithms to mimic real-world data patterns. Instead of collecting data from actual user interactions or real events, synthetic data is produced programmatically to fill gaps in your training dataset.

In the context of vertical AI development, synthetic data is used to create training examples when you don't have enough real user interactions yet. This allows you to bootstrap your AI system with representative examples before real data accumulates.

Example: If you're building a bakery AI assistant, synthetic data might include generated Q&A pairs like "Do you ship conchas to 10001?" or "How long will bolillos stay fresh?" — even though no real customer has asked these questions yet.

What is Organic Data?

Organic data (also called real data or natural data) is data collected from actual user interactions, real events, and genuine system usage. This includes conversations with real customers, actual feedback, real queries, and authentic user behavior.

Organic data is valuable because it reflects real-world usage patterns, genuine user language, authentic edge cases, and actual problems that users encounter. It represents the true needs and behaviors of your users.

Example: A real customer conversation where someone asks "Where's my order?" and then provides their order number, or feedback from staff correcting the AI's response about shipping policies.

Types of Data in Vertical AI

1. Synthetic Data

  • • AI-generated Q&A pairs — Created from your content (menus, policies, FAQs)
  • • Simulated conversations — Tool-using dialogues that show how the AI should interact with APIs
  • • Generated examples — Training examples created before real user data exists
  • • Bootstrap data — Used to start training when you have zero real interactions

When to use: Early stages of development, when you need training data but don't have enough real user interactions yet.

2. Organic Data

  • • Real user conversations — Actual queries and interactions from customers
  • • User feedback — Corrections, ratings, and improvements from real users
  • • Staff corrections — Internal feedback from your team about AI responses
  • • Actual usage patterns — Real-world edge cases and authentic user behavior
  • • Domain-specific Q&A — Real questions and answers from your actual business context

When to use: Always preferred when available. This is the gold standard for training because it reflects real user needs.

3. Mixed Data (Best Practice)

The most effective approach combines both synthetic and organic data:

  • • Start with synthetic data to bootstrap your system
  • • Collect organic data as users interact with your AI
  • • Use synthetic data to fill gaps and cover edge cases
  • • Continuously replace synthetic examples with organic ones as they become available

Why this works: Synthetic data gets you started quickly, while organic data ensures your AI learns from real user needs and behaviors.

How We Use Synthetic Data at VERTEKS.AI

1. Content-Based Generation

We pull your existing content (product pages, menus, policies, FAQs) and generate Q&A pairs that reflect how real users might phrase questions. This includes variations, misspellings, and bilingual variants.

2. Tool-Using Dialogues

We generate synthetic conversations that demonstrate how the AI should interact with tools and APIs (like shipping calculators, inventory lookups, order trackers) before real users start using these features.

3. Quality Over Quantity

We keep synthetic datasets small and curated — a few hundred excellent examples are better than thousands of noisy ones. We focus on high-quality, representative examples that match your domain.

4. Gradual Replacement

As organic data accumulates from real user interactions, we gradually replace synthetic examples with organic ones. This ensures your AI model learns from actual user needs and behaviors.

Best Practices

When to Use Synthetic Data

  • • You have zero or very limited real user data
  • • You need to bootstrap your AI system quickly
  • • You want to cover edge cases that haven't occurred yet
  • • You need examples for tool-using behaviors before real usage

When to Prioritize Organic Data

  • • You have real user interactions available
  • • You want to capture authentic user language and phrasing
  • • You need to learn from actual edge cases and problems
  • • You want to reflect real user needs and behaviors

Key Principle: Synthetic data gets you started, but organic data makes your AI truly understand your users. The best systems use both strategically.

Learn More

See how synthetic and organic data fit into our complete Vertical AI Development Roadmap.

Understand how we use RAG to work with limited data and how embedding models help process your content.

Vertical AI Roadmap →RAG Overview →