Synthetic training data is artificially generated data — created by an algorithm or model rather than collected from the real world — that's used to train, fine-tune, or evaluate machine learning models. It's produced either from scratch, from a schema, rules, or a natural-language prompt, or by modeling the patterns of data you already have. The right generation method depends mainly on your starting point: whether you have existing data to work from — structured or unstructured — or no usable data at all.
What synthetic training data is (and how it differs from real data)
Synthetic training data is data that a model or algorithm produces, rather than data captured from real-world events, users, or systems. It serves the same purpose as collected data — it feeds the training, fine-tuning, or evaluation of machine learning models — but its origin is generation, not observation. That single difference in origin is what the term turns on, and it's worth being precise about, because the most common misconception is that "synthetic" means "lower quality" or "fake." It doesn't. Well-constructed synthetic data is realistic by design; realism is the entire point of generating it.
The cleaner way to draw the line is on provenance rather than fidelity. Real or production data is a record of things that actually happened: it carries genuine events, but also real personal information, collection gaps, sampling bias, and whatever the world happened to hand you. Synthetic data is built to a specification, so you decide its coverage, its edge cases, and its balance, and it carries no direct tie to a real individual. A good synthetic dataset can be statistically close to real data on the dimensions that matter for a model while remaining safe to move and share — which is what makes it useful for building AI training data when real data is scarce, sensitive, or expensive to label.
Generation methods fall into two groups according to what you start with. Synthetic data is built either from scratch — produced purely from a specification, with no underlying dataset — or from existing data as a starting point, modeled on or derived from data you already hold. Which group fits depends on whether you have usable data to work from, structured or unstructured, or none at all.
How synthetic training data is generated: the main approaches
Several distinct families of techniques produce synthetic data, and they differ in how much they invent versus how much they learn from data you already have. Each family suits a different starting point and goal, and most production tools combine more than one.
Rule-based and statistical generation builds data from explicit instructions: handwritten rules, value constraints, and sampling from specified distributions. You tell the generator what a valid record looks like — a date format, a value range, a relationship between two columns — and it produces rows that satisfy those rules. It builds from scratch and is fast and controllable, but it only knows what you encode.
Simulation and procedural generation models a process or an environment and lets it emit data as a byproduct: a physics simulator, an agent-based model of a marketplace, or a rendering engine that produces labeled images. It also builds from scratch, and it shines when you can describe the generating process and need coverage of rare or dangerous scenarios that are hard to capture in the wild.
Generative models — including GANs (generative adversarial networks, where two networks compete to produce and detect realistic samples) and VAEs (variational autoencoders, which learn a compressed representation of data and sample new points from it) — learn the distribution of an existing dataset and draw new examples that resemble it. These work from existing data, so they need a representative sample to learn from. LLM- and agent-driven generation is the newer family: large language models, often orchestrated as agents, produce structured records, free text, or both, either from a prompt and schema or seeded from existing sources.
| Approach | How it works | Best when |
|---|---|---|
| Rule-based / statistical | Generates records from explicit rules and specified distributions | You know the schema and the constraints, and want volume and control |
| Simulation / procedural | Models a process or environment that emits data | You can model the generating process and need rare-event coverage |
| Generative models (GANs, VAEs) | Learns the distribution of a real dataset and samples new instances | You hold representative real data and need more like it |
| LLM- and agent-driven | Models or agents produce structured and unstructured data from a prompt, schema, or seed | You need realistic, mixed-format data and fast iteration |
Generating synthetic training data from scratch
When you have no usable data to start from — a greenfield feature, a model for a domain you've never logged, or a dataset too sensitive to touch — you generate from a specification alone. Tonic Fabricate is a useful example of how this works in practice: you describe what you need with a schema, a set of rules, or a natural-language prompt, and it produces a fully relational dataset with referential integrity maintained across tables. A Data Agent builds the data from your description, and a Validation Agent reviews and refines what it produces, which keeps quality reasonable even when the initial prompt is imprecise.
The decisive advantage of generating from scratch shows up in the labels. Ground truth is the set of correct answers a model is trained and graded against — the labels that turn raw data into training data. When you collect real data, ground truth usually has to be added afterward by human annotators, which is slow and costly. When you generate the data, the labels are produced alongside it: you already know the correct answer because you defined the scenario that created it. That removes the separate annotation step entirely, which is why generating from scratch is so well suited to producing training data without manual ground-truth labeling.
Generation from scratch also reaches beyond database tables. Fabricate produces unstructured outputs in a range of file formats — free text, JSON, PDFs, and other files — and that unstructured output stays referentially intact with the structured data generated alongside it. The same entities, IDs, and relationships line up across a database row, a JSON payload, and a document, so the result holds together as coherent training data rather than a pile of disconnected samples. From-scratch generation suits two uses especially well: populating reinforcement-learning environments with realistic activity for agents to train against, and generating mock APIs that behave like the real services a system will eventually call. The workflow itself is short: specify with a prompt or schema, generate, validate, then operationalize into a pipeline.
The Tonic Advantage: generate, don't annotate. With collected data, labels are a second project — annotators tag examples after the fact, and the dataset is only as good as that pass. Because Fabricate generates the data from a specification, the ground truth is built in: the correct answers exist the moment the data does. For training, that turns "collect, then label" into a single step.
Generating synthetic training data based on existing data
The other path applies when you already hold relevant data but need more of it, broader coverage, or a version that's safe to train on. Existing data becomes the starting point rather than the constraint.
Model an existing database
When you have a real database and need more data that behaves like it, you model the source rather than copy it. Using Fabricate's Live Connect, you connect to a live data source and generate new data that mirrors the real schemas, patterns, and distributions — the column relationships, value frequencies, and cross-table structure that make the data realistic. The output is new synthetic records, not masked copies of production rows, so you can scale a dataset up well beyond what production safely allows while preserving the statistical shape a model needs to learn from.
Synthesize sensitive entities in place
Much of the most valuable training data is unstructured text — support tickets, clinical notes, chat logs — and it's often locked up by the sensitive information inside it. Tonic Textual addresses this with synthesis in place. It extracts free text from where it lives, detects sensitive entities using proprietary NER models — named entity recognition, the task of locating and classifying spans of text such as names, dates, account numbers, and medical terms — and replaces those entities with realistic synthetic substitutes. Crucially, this is the synthesize path, not redaction: instead of blacking out a name, Textual swaps in a plausible fake one, so the surrounding real text and its statistical properties stay intact and the dataset remains useful for training.
Pair the two for scarce data
The two also combine. When unstructured text is both sensitive and scarce, you can de-identify the real set with Textual, then point Fabricate at that de-identified data as a model for generating additional examples. This is data augmentation: you start from a small, safe foundation and expand it into a larger training set without reintroducing the original sensitive content.
The Tonic Advantage: de-identify, then augment. Scarce, sensitive text is a hard case — too risky to use raw, too small to train on well. Pairing two products solves both halves: Textual synthesizes the sensitive entities so the data is safe, and Fabricate models that safe set to generate more of it. The result is a larger, privacy-safe training corpus built from a starting point that was, on its own, too small and too sensitive to use.
Does synthetic training data actually work? Fidelity, evaluation, and limits
Sometimes synthetic data fully replaces real data for training, and sometimes it works best as a complement that fills the gap when collecting enough real data isn't feasible. The deciding factor is fidelity: how faithfully the synthetic data reproduces the patterns a model needs to learn. High-fidelity data can stand in for the real thing; low-fidelity data teaches a model the wrong lessons. The well-documented failure mode is model collapse, where training models repeatedly on their own or other models' outputs causes the distribution to narrow over successive generations — the rare cases and the tails of the distribution thin out until the model degrades. That risk is real, and it's the reason naive or recursive generation is not a free lunch.
This is why evaluation matters as much as generation. The most trustworthy check is downstream task performance: train a model on the synthetic data, then test it on held-out real data and see whether accuracy holds. Teams pair that with distribution checks — comparing the synthetic data's marginal and joint distributions, correlations, and coverage against a real reference set — and with direct benchmarking against real data on the same task. Treated this way, synthetic data isn't taken on faith; it's measured against the same yardstick as any other training input.
The evidence that well-constructed synthetic data can match or beat real data is concrete. In a Tonic.ai research benchmark, an open-source model (Qwen3.5-35B-A3B) fine-tuned only on Fabricate-generated synthetic email data improved on the real-world Enron email benchmark from 80.5% to 86% — outperforming o3 and gpt-4.1-mini — without training on a single real email (Tonic.ai research; the corpus and tasks are published on Hugging Face). The result is a benchmark, not a customer outcome, and it makes the practical point cleanly: when fidelity and structure are right, generated data is a viable substitute for real data, not merely a fallback when real data is unavailable.