Synthetic Data for Model Training

Imagine you need thousands of realistic records to test or train a system, but you are not allowed to use real customer records to do it. One practical answer is to stop thinking about individual records and instead design the shape a record must have. Once that shape is fixed, you can produce as many instances of it as you like. This article follows that idea from the simplest case, a fill-in-the-blank form, through to the way transformed copies of existing data can expand a dataset.

The source gives a concrete case. To create a credit card transaction, you start with a transaction template and use a random generator, such as Faker, to populate each field in that template. The template is the fixed part. The random generator is the source of variation. Each field in the template is a slot, and the generator decides what goes into each slot.

A template is therefore a description of what a transaction must look like, not a transaction itself. Nothing in the template is a real transaction yet. The diagram below traces how the template becomes a usable record: the generator reads the template, fills in each field, and the result is a synthetic record that can be used downstream.

Notice what this pipeline implies. The structure of the output is decided before any generation happens, because the template makes that decision. The variety of the output comes from the generator, one field at a time. This split is what makes the method predictable in shape and varied in content.

Take the transaction template from the source, which has three slots: a transaction identifier, a date, and a time. The template states the expected format of each slot, so the generator must produce values in those formats. The walkthrough below uses an invented template layout to show the process; the slot names come from the source, and the example values are illustrative only.

Templates are not limited to records with named fields. The source lists two other broad uses. The first is documents that follow a specific structure, such as invoices, resumes, tax forms, bank statements, event agendas, product catalogs, contracts, and configuration files. In each case the document has a fixed layout, and the content that fills the layout can vary.

The second use is data that follows a grammar or syntax, such as regular expressions and math equations. The source notes that templates can generate math equations for AI models to solve. Here the template encodes the rules of a valid expression, and the generator chooses the parts that the rules allow. The grammar decides which pieces may appear and in what order; the generator decides which allowed pieces actually appear.

In both cases the split is the same as in the transaction example. Anything the grammar or layout fixes is structure. Anything the rules leave open is a slot for the generator. A generated invoice keeps its header, line-item layout, and totals arrangement, while the amounts, item names, and dates change from one copy to the next.

The source also points to a large-scale use of this idea. DeepMind trained an Olympiad-level geometry model, AlphaGeometry, using 100 million synthetic examples. That scale shows why generating data from structured templates can be worth the effort when real examples are limited.

Templates create data from nothing but a structure. Data augmentation works differently: it starts with data you already have and produces new data by applying simple transformations to it. The source describes this as procedurally generating new data from existing data.

For images, the transformations named in the source are random rotation, cropping, scaling, and erasing part of the image. Each transformed copy is a new training example derived from an original one. The diagram below shows one original image and the copies that each transformation produces.

The reason augmentation works is that the transformations are expected to preserve meaning. The label stays the same while the pixels change. The source gives two examples: a flipped image of a cat should still be a cat, and a slightly cropped image of a soccer game should still be a soccer game.

The main practical motivation appears in the source's discussion of sensitive data. Transaction data is sensitive, so many fraud detection models are first trained on synthetic transaction data generated from templates like the one above. The purpose is to show that the model is feasible before it is given access to real data.

Synthetic data is programmatically generated to supplement real data, and it must be evaluated for quality before training. It became a practical option because acquiring high-quality data is difficult. The source explains that annotating data is hard, but writing annotation guidelines is harder; generating data can be automated to some degree, but verifying that the generated data is correct is harder still. Synthetic data does not remove the need for careful thinking. It changes where the effort goes.

You begin by deciding what behaviors you want the model to learn, and then you design a dataset that demonstrates those behaviors. Dataset design rests on three criteria: quality, coverage, and quantity. A small amount of high-quality data can outperform a large amount of noisy data.

The three criteria are not independent. Coverage determines whether a large dataset is actually useful. Quality determines whether the quantity you collect helps or hurts. The source also notes that increasing the diversity of a dataset is a key way to improve model performance, which sits at the intersection of coverage and quality.

The source is firm on one point: synthetic data must be evaluated to ensure its quality before it is used to train models. This applies to generated data in the same way it applies to real data. A generated example can be wrong, off-topic, or unrepresentative, and training on it can teach the model those flaws.

A data flywheel describes a model that continually improves upon itself. The source notes that, in theory, it is possible to train a model that keeps improving this way with synthetic data. Each round of improvement produces material for the next round, so progress builds on itself. The loop below shows the cycle.

The loop is attractive, but the source is careful about whether it works in practice. The flywheel depends on the quality of what the model feeds back into itself. A cycle that reinforces mistakes is not an improvement. The check in the loop is therefore the condition that matters most.

The source offers a case where feedback clearly helped. In one reported study, about 20 percent of solutions were initially incorrect but self-corrected, which indicated that the model learned from execution feedback and improved its performance. The feedback came from running the code, a signal that reliably separated good output from bad.

The same logic can fail with human annotations. If a human labeler answers a question using knowledge the model does not have, the labeler is effectively teaching the model to hallucinate. The label looks correct to a person but carries information the model cannot reproduce from its own inputs, so the model learns to produce answers without grounds.