Key takeaways
- Use Faker for fake records, scikit-learn for labelled toy data and SDV to mimic real tables.
- Generate LLM training data with seeds, diverse prompts, filtering, an LLM judge and human review.
- Judge synthetic data by fidelity, utility on real data and privacy leakage.
- Augmentation transforms real samples, simulation runs process models, and masking hides real fields.
- Dummy data only fills placeholders, while synthetic data reproduces real patterns.
Synthetic data is artificially generated data that mimics the patterns of real data, used to train and test models when real data is scarce, sensitive or imbalanced. Language models have made generating synthetic text easy, and also easy to get wrong. I built FineTune Studio for QLoRA fine-tuning with base-versus-tuned evaluation, so this page covers generating synthetic data with Python, ChatGPT and generative AI, LLM fine-tuning data, quality checks, tools, and how it differs from augmented, simulated, dummy and masked data.
How do you generate synthetic data using Python?
For fake records, use Faker to create names, addresses and dates; for numeric datasets, use NumPy distributions or scikit-learn generators such as make_classification; to mimic a real table, train a model with the Synthetic Data Vault library.
Also asked as: how to generate synthetic data using python
Faker is ideal for test databases and demos, with locale support including Indian names and addresses. scikit-learn's generators create labelled datasets with controlled noise, class balance and clusters for teaching and benchmarking algorithms. SDV learns from real tabular data, using models such as Gaussian copulas or CTGAN, then samples new rows that preserve distributions and correlations, and its evaluation tools compare synthetic and real data. For text, call an LLM API from Python in a loop with varied prompts, then deduplicate and validate the outputs before use.
How do you generate synthetic data using ChatGPT?
Describe the schema or task precisely, give a few real or hand-written examples, ask for structured output such as JSON, and request variety in topics, difficulty and style. Generate in batches and check every batch.
Also asked as: how to generate synthetic data using chatgpt
Vary prompts with personas, scenarios or seed topics to avoid repetitive outputs, since models tend to produce similar examples when asked the same way. Ask for edge cases and negative examples explicitly. Validate structure with a schema, remove duplicates, and spot-check facts, since the model can invent plausible but wrong details. For larger volumes, use the API rather than the chat interface so you can automate generation and logging. Check the provider's terms on using outputs for training, especially if you plan to train a competing model.
How do you generate synthetic data using generative AI?
Choose a generative model suited to the data type: LLMs for text, diffusion models for images, and GANs, variational autoencoders or specialised models for tabular and time-series data. Then condition generation on the labels or attributes you need.
Also asked as: how to generate synthetic data using generative ai
For text, techniques such as Self-Instruct, from Wang and colleagues, bootstrap many instructions from a small seed set using the model itself. For images, diffusion models create labelled examples of rare cases, such as unusual defects or weather conditions, though domain gaps can remain. For tabular data, CTGAN models conditional column distributions and handles imbalanced categories. Always combine generation with filtering, evaluation against real data and human review, since generative models reproduce their own biases and errors and can memorise training examples.
How do you generate synthetic text data for LLM fine-tuning?
Start with seed examples and a clear task definition, use a strong model to generate diverse instructions and responses, filter them with automated checks and an LLM judge, then have humans review a sample before training.
Also asked as: how to generate synthetic text data for llm fine tuning
Diversity matters most: vary topics, difficulty, phrasing, length and personas, and include edge cases and refusals where appropriate. Methods such as Self-Instruct and Evol-Instruct expand seeds into harder, more varied tasks. Libraries such as distilabel automate generation, scoring and filtering pipelines. Remove near-duplicates and examples that contradict your style guide. Keep a held-out set of real examples for evaluation, since a model tuned on synthetic data can score well on synthetic tests yet fail real users. In FineTune Studio, comparing base and tuned answers on real prompts reveals such gaps quickly.
How do you determine the quality of synthetic data?
Measure three things: fidelity, meaning how closely it matches real data's distributions and relationships; utility, meaning whether models trained on it perform well on real data; and privacy, meaning whether it leaks real records.
Also asked as: how to determine the quality of synthetic data
For tabular data, compare column distributions, correlations and category frequencies, and use tools such as SDV's evaluation reports. Test utility by training on synthetic data and testing on real data, then compare with a model trained on real data. Check privacy with distance-to-closest-record tests and membership inference checks. For text, measure diversity, duplication rates, factual accuracy and label correctness, using automated metrics plus human review of random samples. Track how downstream model quality changes as you add synthetic data, and stop when it stops helping.
What is the difference between synthetic and augmented data?
Augmented data is created by modifying real examples, such as flipping or cropping images, adding noise, or paraphrasing sentences. Synthetic data is generated new by a model, simulation or rules, rather than transformed from a specific real record.
Augmentation increases variety around existing samples and keeps labels mostly valid, making models more robust to small changes. Synthetic data can create entirely new cases, including rare events missing from real data, and can avoid exposing real records. Techniques such as SMOTE sit between the two, interpolating between real minority-class samples to create new ones. In practice, teams often combine both: augmenting real data for robustness and adding synthetic examples to fill gaps. Both need validation, since aggressive augmentation or poor generation can distort labels.
What is the difference between synthetic and simulated data?
Simulated data comes from running a model of a real process, such as a physics engine, driving simulator or agent-based model. Synthetic data is the broader category of artificially generated data, including simulations, statistical models and generative AI.
Simulations encode known rules of the world, so they produce physically consistent data, such as sensor readings for self-driving cars in simulators like CARLA or robot training environments. Their weakness is the reality gap: simulated scenes may differ from messy real conditions. Statistical and generative synthetic data instead learn patterns from real examples, capturing realism but not guaranteed physical consistency. Teams often use simulation for rare or dangerous scenarios and learned generators for realistic variation, then validate both against real-world tests.
What is the difference between dummy data and synthetic data?
Dummy data is placeholder content with no meaningful structure, such as lorem ipsum text or test@example.com, used to fill interfaces and check that code runs. Synthetic data mimics the statistical patterns and relationships of real data.
Dummy data is fine for checking layouts, forms and basic logic, but it cannot reveal problems that depend on realistic values, such as skewed distributions, rare categories or long names. Synthetic data supports performance testing, analytics development and model training because it behaves like production data without exposing real records. Tools such as Faker sit in between, producing realistic-looking individual values without modelling relationships across fields. Choose based on purpose: dummy data for quick checks, synthetic data when realism matters.
What is the difference between synthetic data and masked data?
Masked data is real data with sensitive fields hidden or altered, through redaction, substitution, tokenization or shuffling. Synthetic data contains entirely new records generated to match real patterns, with no one-to-one link to real people.
Masking keeps the original structure and many real values, which preserves usefulness for testing and analysis but can leave re-identification risks, especially when quasi-identifiers such as age, location and dates remain. Synthetic data reduces that risk but may lose subtle relationships or rare cases. Regulations treat them differently in places, since masked data may still count as personal data. Many organisations mask data for internal testing and use synthetic data for sharing or model training. Evaluate privacy risk explicitly for either approach.
Which synthetic data generator can you recommend?
It depends on the data type. For tabular data, the open-source Synthetic Data Vault is a strong start; for fake test records, Faker; for LLM training data, distilabel or your own pipeline around a strong model; for images, diffusion models or simulators.
Also asked as: can anyone recommend a synthetic data generator
Commercial platforms such as Gretel, Mostly AI and YData add privacy guarantees, evaluation reports and enterprise features. For time series, SDV and specialised models handle sequences, while rule-based scripts suit simple patterns. For computer vision, simulation tools generate labelled scenes with perfect annotations. Start with open-source options to learn what quality you need, then consider commercial tools if privacy compliance or scale demands it. Whatever you choose, measure fidelity, utility and privacy on your own data rather than relying on vendor claims.
Sources
- Faker documentationfaker.readthedocs.io
- Synthetic Data Vault documentationdocs.sdv.dev
- scikit-learn, Generated datasetsscikit-learn.org
- Wang et al., Self-Instruct: Aligning Language Models with Self-Generated Instructionsarxiv.org
- distilabel repositorygithub.com
- Xu et al., Modeling Tabular Data using Conditional GANarxiv.org
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
