There are also open-source toolkits you can experiment with:
https://github.com/meta-llama/synthetic-data-kit https://github.com/bespokelabsai/curator
But it still feels very research-oriented. I haven’t found many examples of these pipelines running in real-world products.
I’m curious:
1. Who is using synthetic-data pipelines in production today?
2. What tasks does it actually improve. E.g. fine-tuning smaller models for specific tasks?
Any real-world stories, pointers, or further reading would be hugely appreciated. Thanks!
sargstuff•8mo ago
historically used for processes which make use of time-series / simulations & modeling / forcasting. aka weather forcasting, related points in [0]
2) a) Testing with actual 'sensitive' data may not be possible for security reasons (aka payroll information, stock market price influences)[1]. b) insufficent/incomplete information. aka figure out how well what's known matches 'reality' and/or may suggest areas to look for 'missing' pieces in model.
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[0] : https://www.oreilly.com/library/view/practical-time-series/9...
[1] : https://www.k2view.com/what-is-synthetic-data-generation/
cpard•8mo ago
With synthetic data for large languages models it’s more about QA pairs and reasoning trails for solving complicated problems
sargstuff•8mo ago
----------------
[1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y
[2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structur...
[3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...
cpard•8mo ago
sargstuff•7mo ago
One can escape the original NIL, NULL, None issue by using boolean logic, but that implies rules.
The 'strange thing' about shrodinger's cat, one can never be certain that one didn't pick the context before the cat existed and/or the references after the deceased remains were no longer visible. So, exercise is arguably statistically skewed toward 3/4 deceased, 1/4 alive. Add statistical sampling, and one can get an approximation of where things might be relative to cat life span. Only works one finds at least one instance of an 'alive cat' first. Much easier to just start with Boole's cat to avoid shrodinger's cat issues (aka lambda term). LLM's will happily supply relevant Boole's cat expression with/or without shrodinger's cat input.
Pauli might consider that a half baked cracker.