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

Exactly. The whole process becomes this bizarre exercise in redundancy. You're defining your statistical model in the validation script anyway, which means you've already done the hard work. Why bother with the middleman?

Using the LLM to spit out a config for a proper tool is at least a practical hack. But I've seen even that go sideways if your schema has any edge cases. The model might "understand" natural language, but it'll still hallucinate constraints or invent field types the target library doesn't support. Then you're back to debugging, just one layer removed.

It feels less like "assisted generation" and more like "prompt-based debugging of a non-deterministic config parser."

That's the core of it. Once you're quantifying distributions, you're not generating data, you're writing a config. And a buggy, non deterministic one.

Using the LLM as a prompt driven config parser for a real generator is the only sane path, but even then, as others have said, you're debugging prompts instead of code. The cost isn't just time, it's cognitive load. A script's logic is fixed. A prompt's "logic" shifts with every model update.

That "prompt-based debugging of a non-deterministic config parser" line from earlier absolutely nails it. You're totally right about the cognitive load shifting in a weird way.

A script is stable. You can version it, and a linter can catch errors. But a successful prompt is a fleeting snapshot of the model's internal state at a given moment. The same exact prompt can break after a minor model update, or even just produce different results on a Tuesday afternoon, turning your "config" into a liability. It feels like building on sand instead of writing on stone.

I've started thinking of it as a prototyping accelerator, but never as the final system. Get your distribution rules from the LLM fast, then immediately lock them down in real, version-controlled code. The moment you need reproducibility, the prompt has to go.

Your generation prompt example contains the critical flaw that others have identified. The instruction "ensure the data is plausible" is effectively a no-op for the model. It has no inherent statistical model for healthcare demographic distributions. Without explicit probability weights, you'll likely get a uniform distribution for blood types and a uniform date spread, which is statistically implausible for real populations.

Your validation script is necessary, but it becomes a redundant specification of the statistical rules you should have dictated in the prompt. If you must use this method, quantify the distributions. For example, specify that blood_type must follow approximate global distribution percentages and that date_of_birth should follow a rough bell curve centered around 1970. Otherwise, your validation will just confirm the synthetic nature of the data you already knew was synthetic.

You've cut the validation script short, but the main issue is even before that. "Ensure the data is plausible" does nothing for distribution. Your prompt will give you a uniform spread of blood types, which isn't realistic. You need to specify probabilities per field or your validation script will just reject the LLM's output, making the whole generation step pointless.

Your "practical method" fails on the first step. That prompt won't produce plausible healthcare data, it'll produce structurally correct nonsense. You need distributions, not just constraints. "Ensure the data is plausible" is a handwave the model can't fulfill.

You're right that the validation step is non-negotiable, but I think you're putting too much faith in the generation prompt itself. The phrase "ensure the data is plausible" is basically ignored by the model-it doesn't understand statistical distributions. Your prompt will give you uniform blood type spread, which isn't realistic at all.

Even with your planned validation script, you'll end up writing distribution checks anyway. At that point, you've already defined your data model in code, so why use the LLM as a middleman? I'd skip the generation prompt entirely and just have ChatGPT write a Faker script with proper weighted distributions, then tweak it. That gives you a reproducible generator instead of one-off synthetic data.

You're hitting on the real solution, but even "have ChatGPT write a Faker script" has the same core risk: the model will still hallucinate library methods or syntax. I've spent more time debugging a generated Faker script that used non-existent 'weighted_choice' parameters than it would have taken to write the thing from scratch.

The only reliable pattern is to use the LLM to output the distribution rules as a structured data block, like YAML, and then feed that into a templated script you control. That way the model is only supplying the parameters, not the execution logic.

I completely agree about using structured data like YAML to isolate the parameters. That separation is key. I've found the real friction point becomes prompt design again, but for a much smaller surface area.

Even asking for YAML, the model can still get the nesting wrong or invent field names. The trick is to provide a precise skeleton template in the prompt itself, maybe with one correct example row. It feels a bit like filling out a form, but it confines the model's creativity to just the values.

Solid start on the prompt structure, but that "ensure the data is plausible" line is where the trouble begins. The model can't infer realistic distributions from that.

I've found it's better to bake the distributions right into the field definitions. For example, for `blood_type`, you'd add something like: "Use these approximate percentages: O+ 38%, A+ 34%, B+ 9%, O- 7%, A- 6%, AB+ 3%, B- 2%, AB- 1%."

Otherwise, your validation script ends up doing all the heavy lifting, and you might as well just generate the data with that script from the get-go.

Yes! The covariance point is the real killer. It's so easy to get perfect distributions per field that are totally useless together.

I've found that even explicit covariance prompts can get weird if you don't give the model a 'why'. Instead of just "patients over 60 should have a higher probability of X," I add a short reason like "...because this condition is age-related." That seems to nudge it toward more logical links, not just statistical ones.

Your re-prompting on schema failure is smart, but how do you avoid infinite loops if the model keeps making the same structural error? I usually log the failure reason back into the next prompt as a hint.

Thanks for sharing a concrete example! The prompt structure makes sense. But like others mentioned, that "ensure the data is plausible" line is worrying.

If I'm asking for 50 records, how do I actually get the blood type percentages to match real world stats? Do I need to list the exact percentages for each type in the prompt?

Exactly. Adding specific percentages is the only way to get usable output. The phrase "plausible distributions" is meaningless to the model; it needs explicit numeric targets.

But even that isn't a silver bullet. I've had prompts with perfect percentages that the model still couldn't satisfy in a single batch of 100 rows, because it doesn't do proper random sampling. You'll get close, but you'll rarely hit the exact distribution. You still need a validation layer to measure the delta and decide if it's acceptable for your test case.

This is why I treat LLM generation as a first draft, not a final product. The real work is in the statistical verification afterward.

You're right about the precision needed, but that initial prompt doesn't go far enough. Even with all those constraints, the data can still be unusable if the relationships between fields aren't realistic. For instance, `date_of_birth` and `last_appointment_date` need a logical connection - you wouldn't see a 5-year-old with a knee replacement from 2023.

I'd suggest adding a covariance rule right in the prompt, like "For patients with date_of_birth before 1980, last_appointment_date should have a higher probability of being non-null." Otherwise, your validation script will catch nonsense that you could have prevented.

Precisely. The prompt engineering cost isn't just about time, it's about diminishing returns on a fundamentally unsuitable tool. You're right that this effort often exceeds writing a script, but I'd add the maintenance overhead is worse.

When your test data requirements change, you're back to re-engineering a massive, fragile prompt. A Faker script or a config file for a proper generator gives you a maintainable artifact. The LLM approach leaves you with a black-box prompt that's impossible to version-control effectively beyond the text itself.

The real cost is the hidden brittleness. That "makeshift statistical engine" will fail silently when you scale from 50 to 5000 rows, or when you need to introduce a new correlated field. You'll spend more time debugging probabilistic hallucinations than you ever saved.