Generating Synthetic Data to Train and Evaluate LLMs

Fine-tuning and evaluation both run on data you often do not have: thousands of clean instruction-response pairs, edge cases, labeled examples in your exact domain. Collecting that from humans is slow and expensive, and sometimes the data is private or simply does not exist yet. Synthetic data is the workaround — you have a strong model generate the examples instead.

It has quietly become a backbone of how models are built: much of the instruction-tuning and evaluation data behind today's open models is machine-generated. But synthetic data is only as good as your filtering. Generating examples is trivial; generating examples that are correct, diverse, and actually useful is the whole craft.

What you can generate, and why

Synthetic data shows up in three main places, each solving a shortage of real examples. The common thread is using a capable teacher model to manufacture what would otherwise take an army of annotators.

• Fine-tuning data. Generate instruction-response pairs to teach a smaller model a skill or domain — closely related to distillation.
• Evaluation sets. Generate test questions, tricky edge cases, and adversarial inputs to probe a model where your real logs are thin.
• Augmentation. Expand and diversify a small real dataset by paraphrasing, perturbing, or extending the examples you do have.
• Bootstrapping the private. Stand in for data you cannot use directly — sensitive records, or a product that has no users yet to generate real traffic.

Generation is easy; filtering is the job

The naive approach — ask a model for a thousand examples — produces repetitive, sometimes wrong, often too-easy data that can quietly hurt the model you train on it. The value is entirely in what you keep. Seed generation with real examples for grounding, push for diversity, and then verify and filter hard: check correctness (with a judge or by executing the answer), remove near-duplicates, and drop anything low quality.

Techniques like Self-Instruct and Evol-Instruct formalize this — bootstrapping from a few human seeds, then evolving prompts to be harder and more varied — but the discipline matters more than the recipe. A smaller, rigorously filtered synthetic set beats a huge unfiltered one every time.

Generate, then keep only what verifies

In practice the pipeline is a generate-and-filter loop, where the filter is doing the heavy lifting:

# Generate synthetic examples, then KEEP only the ones that verify and are new.
import random

def make_dataset(seeds, teacher, n=1000):
    data = []
    while len(data) < n:
        seed = random.choice(seeds)               # anchor to real examples for diversity
        ex = teacher.generate(EXPAND_PROMPT, seed) # ask for a fresh (input, output) pair
        if is_valid(ex) and not is_duplicate(ex, data):  # verify correctness + dedup
            data.append(ex)                       # quality control is the whole game
    return data

The is_valid and is_duplicate checks are where the quality lives. Verification can be a judge model, a rule, or actually running the generated code or query; deduplication keeps the set diverse instead of a thousand rephrasings of the same idea.

Anyone can prompt a model for a million examples. The skill is throwing most of them away — synthetic data is a filtering problem wearing a generation problem's clothes.

The traps to respect

• Model collapse. Train repeatedly on a model's own outputs and quality degrades; keep real data in the mix and use a stronger teacher than the student.
• Inherited flaws. The teacher's biases, blind spots, and confident errors get copied into your dataset — verify, do not assume.
• Eval contamination. Never let synthetic training data leak into your test set, or your evaluation numbers become fiction.
• Diversity collapse. Unconstrained generation clusters around a few patterns; seed widely and measure variety, not just volume.
• Terms and licenses. Some providers restrict training competing models on their outputs — check what you are allowed to generate and use before you ship it.

The bottom line

Synthetic data lets you fine-tune and evaluate when real data is scarce, private, or expensive — and it is how a lot of capable models actually get built. Generate from a strong teacher, seed with real examples, and filter ruthlessly for correctness and diversity. Then mix in real data and hold out a real evaluation set so you can prove it helped.

Treat it as manufacturing with a strict quality line, not a magic data faucet. The generation is the cheap part; the verification, deduplication, and honest evaluation are what turn synthetic output into data worth training on.