Real data is information recorded from actual events, people, or documents. Synthetic data is information generated by a model, simulation, or rules to substitute for real data. Real data serves as the accuracy reference. Synthetic data supplies volume, rare cases, and sharing without real records. Teams commonly combine both, keeping real data for evaluation.
Key Takeaways
- Real data serves as the reference for accuracy, so a held-out real test set stays in the pipeline even when most training data is generated.
- Synthetic data adds volume, rare cases, and sharing without real records, but it reflects only what its generator learned or was told.
- Model collapse appears when models train indiscriminately on model-generated content, so what you keep matters as much as what you generate.
- Curation changes model quality: in an Adaption evaluation, models trained on checklist-filtered examples outperformed models trained on randomly sampled ones.
- Starting requirements differ by generation method: some generators need real data to learn from, and specification-based generation starts from a description of the behavior.
How Do Synthetic Data and Real Data Differ?
Synthetic data and real data differ in origin, which drives their trade-offs in privacy, cost, coverage, and fidelity.
| Real Data | Synthetic Data | |
|---|---|---|
| Ground truth and fidelity | Records actual events, including noise and bias | Matches real data only as closely as the generator learned it, so fidelity varies by method and has to be tested |
| Privacy | Can contain personal records, and access is often restricted | Can avoid real records, but privacy obligations can still apply, so test re-identification risk |
| Cost structure | Collection, cleaning, and labeling costs grow with volume | Low cost per example once a generator exists, with cost moving to building and validating it |
| Rare-case coverage | Limited to what was recorded | Can target rare cases on demand, within what the generator can produce |
| Role in evaluation | Reference for held-out test sets | Useful for training and stress tests, with results checked against real data |
| Starting requirement | Access to the source records | Depends on the method: some generators need real data, and others start from a description |
| Permitted use | Governed by consent, licenses, and privacy rules | Governed by the terms of the model or tool that generated it, and some providers restrict training on their outputs |
What Is Real Data?
Real data is information collected from actual events, people, instruments, or documents, such as customer chats, sensor readings, transactions, and scanned records. Real data is the reference for what happened and for whether a model is right, but it also carries the noise, gaps, and sampling bias of the world.
Strengths of Real Data
- It shows the true distribution, including the rare cases that occurred.
- It supplies the held-out test set that every other kind of data is checked against.
- It carries the wording, dialect, and domain detail of real users.
Limits of Real Data
- Collection bias: real data over-represents what was easy to record.
- Gaps and label noise: fields go missing, labels conflict, and old scans lose detail.
- Access: consent, privacy rules, and contracts can restrict use.
- Cost: collecting, cleaning, and labeling become more expensive as volume grows, especially for rare or regulated cases.
What is Synthetic Data?
Synthetic data is data produced by a program, model, or simulation instead of recorded from real events. It is built to follow the structure and patterns of the cases it represents, and teams use it to supply examples that are scarce, private, or costly to label.
Strengths of Synthetic Data
- Volume on demand: examples are generated at the size a team requests.
- Rare cases: generation can target events that collected data seldom contains.
- Attached labels: generated examples can arrive with labels or completions.
- Sharing without real records: teams can share data that holds no customer or patient records.
Limits of Synthetic Data
- Inherited assumptions: the data reflects only what the generator learned or was given.
- Repetition: generated examples can grow more repetitive as volume rises.
- Privacy obligations: personal data rules can still apply, so teams assess re-identification risk.
- Model collapse: repeated training on model output removes rare examples.
How Do You Test Synthetic Data Against Real Data?
Test synthetic data by training a model on it and scoring that model on held-out real data, then compare its fidelity, coverage, diversity, and privacy risk with the real source records.
Utility
Utility measures whether a model trained on synthetic data performs on real tasks. It is decisive because synthetic data can resemble real data statistically and still train a worse model. Where you have enough real data, compare the model’s score with one trained on real data.
Fidelity
Fidelity measures whether synthetic values and relationships resemble the real data. Compare distributions and correlations in the synthetic set with those in the real source records. This check applies mainly to structured data.
Coverage and Diversity
Coverage and diversity measure whether synthetic data spans the topics, classes, and rare cases that real data shows, without repeating itself. Compare category frequencies with the real set, and count duplicates at the volume you plan to use, because repetition tends to grow as volume rises.
Privacy
Privacy testing measures whether synthetic data reproduces or reveals real records. Compare synthetic records with the real source records for matches, and assess re-identification risk before sharing the data or training on it.
Acting on the Results: Filter Before Training
When testing finds weak or repetitive examples, remove them before training. Checklist filtering in the Adaptive Data platform does this with an AI agent that writes a checklist of yes-or-no requirements and removes any example that misses an applicable one.
Adaption’s October 2026 evaluation found that models trained on 10,000 checklist-filtered examples had relative win rates 9 to 13 percent higher than models trained on 10,000 examples sampled at random from a pool of 70,000. The gains are relative and were measured on Llama3.3-70B. The evaluation compared selection methods, not data origins, so it shows that choosing which examples to train on changes results, not that one data source beats another.
Can You Combine Synthetic Data and Real Data?
Teams combine synthetic data and real data by giving each a separate job: real data anchors training and supplies the test set, and synthetic data adds coverage where real examples are thin, private, or missing.
Three patterns show how to combine them:
- Real core, synthetic coverage. Train on collected data, then add generated examples for the rare cases and underrepresented classes the real set lacks.
- Generation from real examples. Expand a small real set with new variations, languages, or localizations.
- Real data in every mix. Keep a share of real data in training and monitoring, and avoid training repeatedly on a model’s own unchecked output.
How Did AI Singapore Combine Real and Generated Data?
A one-month pilot reported in August 2026 shows generation from real examples at work. AI Singapore (AISG) and Adaption built training data for SEA-LION, AISG’s family of open-source large language models for Southeast Asian languages:
- Real source. AISG’s team sourced the real seed data.
- Generated from real examples. The Adaptive Data platform generated about 750,000 new samples by localizing those real examples, which grew coverage from seven to 11 languages and regional variants.
- Independent review. AISG’s team designed the evaluations and reviewed the outcomes.
Read the AI Singapore pilot for the full results.
For generation from a small real set, teams can use Expand Your World, a feature of the Adaptive Data platform that scales a dataset from as few as 10 examples to up to 2,420 examples across 242 languages and localizations.
How Do You Choose Between Synthetic Data and Real Data?
Choose based on the constraint you face. Use real data when accuracy must be checked against what happened, synthetic data when real examples are scarce, restricted, or costly to label, and both when a model needs coverage and a trustworthy test.
| Challenge | Best Choice |
|---|---|
| Real examples are scarce | Both |
| Privacy or access rules limit real records | Synthetic data |
| Rare events matter, such as fraud or uncommon conditions | Synthetic data |
| No example data exists for the behavior | Synthetic data |
| You need evaluation sets, reference answers, or regulated decisions | Real data |
| You are monitoring a deployed model | Real data |
Restricted records, behaviors that collected data rarely shows, and missing examples all leave a team without data to start from. Invent a Dataset, Adaption’s dataset generation product, builds a training-ready dataset from a description of the behavior a model should learn, with no seed data, schema, or labeling guide. A team describes a task, such as summarizing business documents to an organization’s standards or classifying customer requests across departments, and sets the dataset’s size and scope in the same flow.
Generating data at volume brings its own risk: repetition. In an October 2026 evaluation of the Invent a Dataset API against five frontier model APIs, Invent maintained highest quality and diversity, simultaneously, at sizes from 200 to 20,000 samples. Adaption also permits commercial use of datasets Invent a Dataset generates for downstream model training.
Author
Adaption TeamDate