TL;DR
- Real data is information recorded from the real world, such as measurements, documents or records of what people did. Synthetic data is made by a computer program to imitate real data. It often fills gaps where real examples are rare or private.
- Neither replaces the other. Use synthetic data where real examples are too few or too sensitive to share, and keep real data for checking whether an AI is actually right.
Key Takeaways
- Real and synthetic data go wrong in different ways. Real data has gaps and damage, while synthetic data repeats the blind spots of the program that made it.
- Looking real is not the same as being useful. Synthetic data can look convincing and still teach an AI the wrong patterns, just as a well-written answer can still be wrong.
- Using both together works best. In our tests, invented answers with a known correct score got too many points 30 times out of 200. That exposed a flaw in our grading rules.
- The choice still matters after launch. An AI trained mostly on synthetic data can fall out of step as the real world changes, a problem called drift.
Synthetic data vs real data is a choice between recording the world and generating a stand-in for it. Teams often frame it as quality against convenience, but the two fail in different ways and suit different stages of an AI pipeline, so the useful question is where each one belongs.
We use both in Groundtruth, Eigenform’s open source dynamic benchmark: real exploration reports as the source, with model-generated questions and fabricated test answers built on top. The examples below come from that work. This article describes the trade-offs; it does not walk through generation code or tools.
What Is Real Data?
Real data is information collected from actual events, people, instruments or documents, rather than output from a model or simulation. It records the world as it happened, including noise, gaps and sampling bias, which is why it serves as the reference for training and for checking whether a model is right.
“Real” does not mean clean. Sensors drift, forms go unfilled, and old scans lose detail. For one of our document sets we downloaded 190 candidate reports, just over eight million words, and ranked them by measured legibility before choosing any.
In one 1920s report, scanning errors left 18% of the pages too damaged to rely on. The data was real, and a fifth of one source was unusable.
What Is Synthetic Data for AI?
Synthetic data is artificially generated data that mimics the statistical properties or structure of real data, or covers cases real data lacks. The output can be tables, text, images or sensor streams, and teams use synthetic data for AI to train and test models, and to share data they cannot share as it is.
Three generation methods cover most cases:
- Simulation uses a model of a physical or business process, such as a driving or physics engine, to produce examples with known properties.
- Generative models (language models, diffusion models, GANs) learn a distribution from real examples and sample new ones from it.
- Rule-based augmentation transforms existing records, for example by adding noise, cropping an image or filling a template.
All three depend on something real: a process someone understands, a dataset the model learned from, or records to transform. In that sense, synthetic data draws on the world rather than standing apart from it.
Synthetic Data vs Real Data: Main Differences
Synthetic data vs real data differs in where examples come from, what they cost, how they treat privacy, how well they cover rare cases, and how faithful they are to the world.
Key differences
| Dimension | Real data | Synthetic data |
|---|---|---|
| Provenance | Collected from actual events, instruments, documents or users | Produced by a simulation, generative model or rules |
| Cost | Collection, cleaning and labeling; high for rare or regulated data | Cheap per example once the generator exists; cost moves to building and validating it |
| Privacy | May contain personal or sensitive records; access is restricted | Can avoid direct identifiers, but is not automatically private if the generator memorizes |
| Coverage | Limited to what happened and was recorded; rare events are thin | Can target rare cases on demand, but only ones the generator can produce |
| Fidelity | Faithful to reality, including its noise and bias | Faithful only to what the generator learned or was told |
Real data limitations
Real data only covers what someone collected. Collection bias means it over-represents what was easy to record. Gaps and label noise are common, and privacy rules or cost can restrict access. It also arrives damaged: extraction errors from turning PDFs into usable text are the quietest, because they sit inside the text layer where re-reading cannot see them.
Synthetic data limitations
Synthetic data limitations come from the generator. It inherits the generator’s assumptions and can amplify them, and it misses edge cases the generator never produces. Realism does not guarantee usefulness: realistic data can look right while breaking the relationships between variables a model needs to learn. Fluency is the same trap in text, since an LLM judge given a loose scoring instruction rewards answers that sound like the domain whether or not they are correct.
In our work the generator repeated itself. When we drafted questions without a plan, 9 of the last 22 on one build collided with earlier ones. The deeper risk is recursive: a 2024 Nature study found that indiscriminate training on model-generated content causes defects in which the tails of the original distribution disappear.
Our own work on self-training on unchecked output reaches the same conclusion: errors compound when nothing outside the model decides what to keep.
Use cases where each is typically chosen
Teams typically choose real data for evaluation sets, for reference answers, for regulated decisions that need actual outcomes, and for monitoring a deployed model. They turn to synthetic data generation for rare-event coverage, for testing pipelines, for balancing classes, and for drafting labeled examples that people then check.
Groundtruth shows the second pattern. A large model generated 35 questions from historical tenement reports, each citable to a specific book, page and line, and a geology expert vetted every question and grading criterion before use. The model drafted the questions; the sources and the expert decided what was correct.
When to Use Which
In synthetic vs real data machine learning work, the choice follows the constraint you face:
- Data scarcity. Use synthetic data to augment, then check the result against a real held-out set. Augmentation that never meets real data remains a guess.
- Privacy or regulatory limits. Synthetic data can reduce exposure, but test whether the generator reproduces real records before treating it as safe, and take legal advice on what counts as personal data.
- Rare-event coverage. Simulation is often the only practical way to get enough examples, provided the simulator reflects how the event actually happens.
- Cost and timeline pressure. Synthetic examples are cheap to produce and not cheap to trust. Budget for validation, not just generation.
- Need for ground-truth accuracy. Use real, verified sources. The reference answers you score a model against, what we cover under ground truth data for AI, should trace to real material, because every score is a comparison against them.
How to Combine Synthetic and Real Data
Synthetic data vs real data is rarely an either-or choice. Combining works best when each kind of data does the job it suits, and three patterns recur.
- Synthetic for coverage, real for fine-tuning and evaluation. Some teams add generated text to pre-training corpora or augment a fine-tuning set, and keep real data for the final fitting and for the test set that judges the model.
- Real-anchored validation of synthetic sets. Train on the synthetic set, test on real data, and compare. We apply a version of this to our grader: fabricated answers with known expected scores run through the production judge before it grades any real model. Across 200 such answers on two projects, 14 and 16 scored higher than they should have, because the rubric rewarded the same idea twice. Synthetic data found the defect, and real answers would have hidden it.
- Monitoring for synthetic-induced bias. Track duplication and diversity in generated data, keep a share of real data in every training mix, and avoid training on a model’s own unchecked output.
How to Prevent Model Drift
Data choice shapes drift risk. A synthetic set describes the world as the generator saw it at generation time, so a model trained mostly on it has no signal when that world moves. The first step is telling model drift vs data drift apart: data drift is a change in the inputs, and model drift is the loss of performance that may follow.
Monitoring catches what data choice alone cannot, because it measures the deployed model against fresh real data. On our 50-question sets, the smallest gap we can tell from noise is 0.27 to 0.45 points out of 10, so a threshold set inside that range fires on nothing.
This is a bridge, not the full treatment. The practical rule from the synthetic data vs real data trade-off is to keep real data in the loop after launch, since real data is what shows when a synthetic-heavy model has fallen behind.
FAQs
How do you compare synthetic data vs real data quality?
Check three things: fidelity (does it match the statistics of real data), utility (does a model trained on it perform well on real test data) and privacy (does it reproduce real records). Utility is the most decisive test. A set can look statistically close and still train a worse model.
What is the difference between synthetic data and data augmentation?
Data augmentation transforms existing real examples, for instance by rotating an image or adding noise. Synthetic data is a broader term for any generated data, including entirely new examples from a simulation or generative model. Augmentation is one rule-based way of producing synthetic data, and it stays close to the original records.
Is synthetic data the same as anonymized data?
No. Anonymized data is real data with identifying details removed or masked. Synthetic data is newly generated and contains no original records, at least in intent. Neither is automatically safe: anonymized data can be re-identified, and a generator that memorizes can reproduce real records, so both need testing.
What types of synthetic data are there?
Common types are tabular records, text, images, audio, time series and simulated environments. Teams often generate tabular and time-series data to share it without exposing real records, and more often use images, text and simulated environments to expand training sets or cover rare scenarios.
Does synthetic data need real data to start?
Usually, yes. Generative models learn from real examples, augmentation transforms real records, and simulation needs a process someone has measured or understands. Purely rule-based or simulated data can start without a dataset, but its realism depends on how well the rules describe the real world.
