Synthetic data is fueling impressive leaps in AI capabilities, from Grok 4 to GPT-5. These leaps would have been impossible with real-world data alone. Why is that?

Advanced applications like foundation models require immense amounts of data to learn complex patterns. The problem is, real-world data is scarce, expensive, and often filled with privacy risks, limiting what AI models can learn.

For decades, one proposed workaround was synthetic data: artificial information created by computers. Unfortunately, creating the most valuable types of data has historically been out of reach.

Until recently, computers could only follow rigid statistical rules, which produced sterile data that lacked the messiness of reality. They could not generate the most valuable information, because most of it is unstructured: the text in a doctor’s note, the audio of a sales call, the image from a factory floor.

This old picture of synthetic data is rapidly changing and no longer accurate. What can be done with synthetic data today is vastly different from even three years ago because of large language models (LLMs).

LLMs finally allow computers to generate abundant, high-fidelity, and multi-modal synthetic data. They act as the universal translator between the structured language of computers and our unstructured reality, making it possible to generate data that more closely resembles a model of our world.

From Data to Reality: A New Generative Workflow

Before LLMs, a computer could generate a table of numbers or sample from a closed set of labels. Translating that into a world model closer to our own, like creating a written report, was nearly impossible, or highly unrealistic. The critical new capability of LLMs is to move fluidly between structured data and unstructured reality, even across different formats like audio or images.

Additionally, abundant compute and open-weight LLMs have made it easy, inexpensive, and compliant to generate reams of synthetic data on as-needed basis. By contrast, proprietary model providers like OpenAI typically place restrictions on synthetic data generation to prevent training data from leaking.

Consider generating synthetic medical data. An LLM enables a workflow that was previously impossible:

1. Start with Structure: You can begin with a simple, structured JSON object containing the core facts of a case.

{
  "patient_id": "SYNTH-734",
  "diagnosis": "Acute Myocardial Infarction",
  "age": 68,
  "key_labs": { "Troponin": "2.5 ng/mL" },
  "medication_prescribed": "Aspirin, Nitroglycerin"
}

2. Generate Unstructured Text: An LLM can take this structured data and generate a rich, unstructured clinical narrative. It interpolates the facts with domain-specific knowledge to create a realistic note a doctor would write, adding plausible context that was not in the original data.
   "Patient is a 68-year-old male presenting to the ED with chest pain. EKG shows ST-segment elevation. Lab results are significant for a Troponin level of 2.5 ng/mL, confirming an acute myocardial infarction. The patient was administered Aspirin and Nitroglycerin..."

3. Translate to Other Modalities: The LLM can then use this unstructured text as a script to generate other data formats. It can create a synthetic audio file of a physician dictating the clinical note, or a simulated audio consultation where a doctor explains the diagnosis to the patient. It can even generate an illustrative medical image, such as a simplified EKG strip or a diagram of the affected coronary artery, that is consistent with the diagnosis.

This process is a two way street: we can also go from unstructured data in any modality and generate a structured output. For example, we can take an audio file, have the LLM transcribe it to a text note, and then extract the key information into a structured JSON file.

This ability to seamlessly map data across representations, from structured tables to unstructured text, audio, and images, is what now allows computers to generate synthetic data that has the depth and texture of the real world.

What Synthetic Data Unlocks: New Applications

Because LLMs provide a universal translation service for all types of data, they are unlocking new applications that were previously impossible. Synthetic data is no longer a niche statistical tool, but a profound new way to create high-fidelity simulations of reality that unlock a slew of new opportunities.

For example, here are several applications for synthetic data we have noticed are gaining popularity now that we have powerful LLMs at our fingertips:

Targeted Curation for Model Training

Massive amounts of unstructured data power LLM training. Model creators use smaller, more curated sets of unstructured labeled data to further improve the LLM for specific tasks. This last process, known as post-training, often uses synthetic data. Many of the newest model releases are trained entirely on synthetic data. Elon Musk has gone as far as to say “Most training in the future will actually be inference for the sole purpose of training (aka synthetic data generation)”, as demonstrated by the impressive leaps in Grok-4’s capabilities.

Model Distillation and Fine-Tuning

Perhaps even more importantly, synthetic data lets independent developers and organizations inexpensively post-train foundation models via distillation and fine-tuning. They can quickly and inexpensively create curated datasets from larger, open-weight foundation models.

This is extremely useful for task-specialization, creating models with specific world-knowledge, and model personalization. While many closed-weight foundation model companies continue to prohibit utilization of their most capable models for these purposes, large open-weight models fill the legal gap needed for such purposes.

Improving Information Retrieval Systems

Systems like Retrieval-Augmented Generation (RAG) need a clean, structured knowledge base to function. LLMs can take a company's messy, unstructured documents (PDFs, Word docs, transcripts) and translate them into a structured database. This newly organized data becomes the "ground truth" the RAG system uses to provide accurate, context-aware answers.

Additionally, LLMs can augment structured data with hypothetical unstructured data to boost search and retrieval. For example, LLMs can generate hypothetical question-answer pairs that match a document, making it easier to feed data into traditional search algorithms and get better matches.

High-Fidelity Simulation of Complex Systems

LLMs can provide synthetic data to power complex simulations. With an LLM, a developer can generate realistic data to model complex, dynamic systems – simulating sociological trends, financial market behavior under stress, or biological responses to a new virus. This allows for testing and prediction in a risk-free environment.

Bootstrapping and Anonymization

AI teams are using LLMs to solve the "cold start" problem for new products where no data exists by generating a foundational dataset. For existing products, an LLM can create perfectly anonymized, synthetic versions of user data that retain statistical properties for testing and analysis without compromising privacy.

Where does value accrue in a world with synthetic data?

When high-quality data can be created at will, what then becomes valuable? The answer is twofold.

First, the value of real, human-generated data skyrockets. It becomes the gold standard, the irreplaceable ground truth used to seed, steer, and validate every synthetic dataset. This human data is the anchor to reality, ensuring that the models we build are not just statistically sound, but reflect the world as it truly is.

Second, value flows to the new generation of platforms built to master this abundance. The new frontier isn't just about making data, but about the craft of using it. This requires sophisticated systems for its creation, curation, and evaluation. 

Lastly, the value now flows from the foundational model builders to those deploying them. As scaling laws prevail it is likely that synthetic data – not compute – will be the tool to shape, remix, customize, and improve LLMs for the foreseeable future. The teams who win will be those with the best tools to version their datasets like code, to rigorously test for quality and bias, and to deploy them with confidence. The advantage shifts from who has the data to who can wield it most effectively.

How can I generate synthetic data with LLMs?

We created Sutro for this new era of development. It’s a platform designed to give builders an unfair advantage, turning the complex art of data generation into a simple, creative workflow. Sutro was built for teams that care about craft and speed, giving you:

• Built with speed and experimentation in mind: Our distributed, high-throughput inference infrastructure allows runs to complete in minutes, not days. Jobs are detached, parallelized, and observable as they run so you can understand results and move onto the next experiment faster.

• Low cost means more experiments, higher data volumes (or both): Sutro is very inexpensive at scale, allowing you to do more with less. Run large parameter sweeps, automatically test prompts, or generate token corpuses you never knew you could afford.

• Scale without the friction. Move from small experiments to billion token jobs with one code change. 

• Effortless collaboration. AI is a team sport. Sutro provides a central environment to create, share, and perfect your datasets with your entire team.

We’ve also started to release helpful guides complete with code examples and open-source datasets that break down some of the methodologies that can be used for synthetic data generation, and will continue to release more in the coming months.

If you’re just getting started with LLM synthetic data generation, we recommend reading: Generating Representative Synthetic Data with LLMs - Zero to Hero.

If you’re looking for simple privacy preservation techniques, you can check out: Synthetic Data For Privacy Preservation.

And when you’re ready to get started, you can request access to Sutro cloud beta today.