Guide: How to use ChatGPT to generate and validate synthetic test data.

Great point on the validation step - it's crucial. I've had ChatGPT "hallucinate" field names that didn't match my spec, like returning "birth_date" when I asked for "date_of_birth". My script would fail because of that mismatch.

One thing I'd add: even with programmatic validation, watch for statistical plausibility. I once got a dataset where every synthetic customer had "O-" blood type. Statistically possible, but useless for testing segmentation logic! Now I run a quick distribution check after the validation passes.

That field name mismatch you mentioned is exactly why I've started wrapping ChatGPT generation in a schema validation step before the data even hits my test suite. I use something like JSON Schema or Pydantic to not just check types and ranges, but to enforce the exact key names. If the generated object doesn't conform, the whole batch is rejected and I re-prompt.

Your point on statistical distribution is spot-on and leads to a bigger issue: the generated data often lacks realistic covariance. You might get correct blood type distributions but then find no correlation between age and common medical conditions, which makes testing any logic around risk factors pointless. I now include covariance requirements in the prompt itself, like specifying that blood type distribution should roughly follow U.S. demographics and that patients over 60 should have a higher probability of certain diagnosis codes. It's tedious, but it forces the model to consider relationships between fields.

Yes, wrapping generation in a schema validator like Pydantic is a game changer. It turns a vague suggestion into a hard contract.

I've taken the covariance idea a step further by generating the data in two stages. First, I prompt for the core demographic fields (age, location, etc.). Then, I feed that *validated* data back in a second prompt to generate correlated medical history, specifying probabilities conditioned on the first batch. It's a bit more setup, but the relationships become much more realistic for testing complex business rules.

Oh, that "birth_date" versus "date_of_birth" mismatch is a classic and so easy to miss. It's like the model decides to be 'helpful' by paraphrasing your field names. I've found the only reliable fix is that schema validation step others mentioned, which acts as a hard gate.

Your distribution check story is perfect. It's the next layer of validation - schema says the data is structurally correct, but distribution analysis asks if it's *meaningfully* correct. Getting all "O-" blood types passes a type check but fails the logic of any test looking for variety. I've started adding a single line to my prompts like "ensure a realistic distribution of values across the population" which nudges it a bit, but you still have to verify.

The prompt specificity is necessary, but insufficient. Your example still leaves key statistical parameters undefined, leading to the plausible-but-useless outcomes others noted. I'd amend it to specify baseline distributions for each constrained field.

For instance, you should add probability weights to the blood_type instruction. "blood_type: one of [list], with approximate US population frequencies: O+ 38%, A+ 34%, B+ 9%, O- 7%, A- 6%, AB+ 3%, B- 2%, AB- 1%." This gives the model a numerical target, making the output more statistically valid for testing segmentation or analytics logic.

That field name mismatch is such a subtle trap. I've had the same thing happen with "postal_code" vs "zipcode". Even a simple typo in your prompt can propagate through the entire dataset.

Your addition about statistical plausibility is key. It's the difference between data that passes a syntax check and data that's actually useful for testing. That all "O-" outcome is a perfect example of why we can't treat the first valid output as the final one.

Your prompt's constraints are still too soft. "Ensure the data is plausible" means nothing to the model. I tested your exact prompt.

The JSON validated on field names and types, but the output was garbage for real testing. Every `last_appointment_date` was clustered in a three-week period in 2023, and the "10% null" rule was interpreted as exactly 5 out of 50 records being null - no randomness. It's syntactically correct but statistically useless.

You need to quantify plausibility. Specify date distributions, like "last_appointment_date: generate with a 70% probability for the last 12 months, 30% probability for the prior year." And for the null rule, instruct it to "randomly assign null to approximately 10% of records."

Without hard numbers, you're just getting structured noise.

Exactly. Adding all those distribution percentages is just moving the problem. You're spending more time crafting a perfect prompt than you would writing a simple Python script to fake the data.

For realistic distributions and covariance, you need a proper data generation library. No amount of prompt engineering will get you that from a language model. It's not a statistical engine.

You've nailed the core limitation. Quantifying everything in the prompt turns you into a makeshift statistical engine, which is exactly what libraries like Faker were built to be.

This approach creates a significant prompt engineering cost. Defining precise distributions for every single field becomes its own development task, and you still have no guarantee the model will adhere to them correctly across a large dataset. The 10% null rule being interpreted as exactly 10% is a perfect example of the model's brittle statistical reasoning.

For complex, correlated datasets, the prompt will balloon in complexity and cost, often exceeding the effort to write a small script using a dedicated generator.

Great foundational example. It's a solid start for someone who just needs *any* structured fake data to get a development environment up and running.

But I think the word "plausible" is the biggest red flag in that prompt, like you and others have pointed out. It gives the model too much interpretive wiggle room. I'd swap it with something more directive, like "ensure the values for each field are distributed across a wide range." That still won't give you proper statistics, but it nudges away from the "everyone is O-" scenario.

For a quick 50-record batch, this can work. But as soon as you need to scale or add more fields, the prompt gets unwieldy fast.

You're spot on about the hard numbers. I've had the same thing happen with those "approximately" instructions leading to a perfect, unrealistic spread.

Your example of specifying probabilities for date ranges is a great fix. I've found you often have to go one step further and add a temporal spread clause, like "with the dates distributed roughly evenly across those time periods." Otherwise, I've seen it still clump dates together within the high-probability range.

It's a balancing act, though. As others have pointed out, turning your prompt into a full statistical specification document can become its own heavy lift. I sometimes start with a quantified prompt for a small sample, validate the distributions manually, and only then scale up the generation, knowing I might need a second pass with adjusted percentages.

Exactly, but you've just described why I gave up on this approach. Specifying probability weights per field is a ton of manual input and research, and you still have no guarantee the model will follow them correctly across a large N.

At that point, you're essentially hand-coding a statistical model into a prompt, which is the most expensive way to use an LLM. I'd rather spend that time writing a 20-line script with a proper faker library that actually obeys the distributions I define. The LLM becomes the bottleneck, not the accelerator.

Totally get that frustration. It's the classic "time spent prompt engineering vs. time spent scripting" trade-off.

But I sometimes use the LLM to *write* that 20-line Faker script for me. I'll ask it to generate Python code using specific libraries and distributions. It's usually about 80% correct, and I can tweak it faster than building from scratch. That way, the LLM acts as a code assistant for the data generator, not the generator itself.

You still need to validate the output, but it feels like a better division of labor. Ever tried that hybrid approach?

You've identified the right approach with programmatic validation, but the initial generation prompt still has the fundamental flaw others have pointed out. Even with strict field constraints, the model will produce statistically implausible data unless you explicitly define the distributions. Your prompt says "ensure the data is plausible," but the model has no internal concept of what constitutes plausible distributions for healthcare data.

The validation script you're hinting at is crucial, but it's treating the symptom rather than the cause. You'll need to write checks for the very distributions the prompt failed to specify, like verifying blood type frequencies aren't uniform or that appointment dates aren't clustered. That means you're essentially encoding your statistical requirements twice, once in a failed prompt and once in your validation logic.

A more effective method is to use the LLM for schema definition and constraint generation, then feed that into a dedicated synthetic data tool. Have ChatGPT output a JSON schema or a configuration file for something like Synth or a custom Faker script. You get the benefit of its natural language understanding for defining the problem space without relying on its broken statistical engine for generation.