Exactly! Finetuning needs at least 10 examples to even work. That’s why Promptrepo begins with prompting and schema-based generation when teams have little or no data. As they gather more examples, it gradually shifts to fine-tuning. It’s the classic cold start problem and we’ve simplified it for product teams who want to launch quickly but improve accuracy over time.

Can you share an example of such real world win where fine tuning was less effective ? I’m curious about sample business cases.

Thanks! Yes, that's a popular request from other developers as well. That's why our basic plan is self managed plan (Bring your own OpenAI account) - https://promptrepo.com/finetune/pricing.html

We have one month free trial as well. Free free to ping me if you need more time.

Finetuning is technical, but OpenAI hides things like layers, learning rates, and uses LoRA under the hood. You just upload examples - usually around 50.

But even that’s too much for most business users. Choosing input/output fields, generating and validating JSONL still feels like coding. That’s why we built PromptRepo: it turns finetuning into a guided, no-code workflow using spreadsheets.

I understand your concern. We’ve had cases where short-term users abused our product for phishing, which led us to remove the monthly plan initially. To address that without locking out genuine users, we’ve added an option to extend the trial for up to 3 months before choosing a monthly or yearly plan. Feel free to message me if you’d like more time.

It depends on the use case. For many business workflows, where structured data is key - spreadsheets are already the source of truth. But for chat-based or unstructured tasks, a custom UI might make more sense.

Our target customers are product teams like ours. I’m the technical founder and was building a feature to extract product prices from unstructured text. But my co-founder, who understands all the pricing formats — is non-technical. I built Promptrepo, so he could finetune the model himself and improve accuracy without relying on me. My bet is that this same dynamic exists in many other product teams.

Thanks again for the feedback and recommendation. We’ll update our site with clearer examples and target use cases!

Thanks for the heads up. Looks like a traffic spike from HN. Can you please check now?

Yeah, Google Sheets is a surprisingly powerful interface for business teams, especially when they’re the ones curating the data. Curious to see how Promptrepo fits into your workflow. Happy to help if you explore finetuning on top of your existing setup.

Ideally, you want to start small and iterate. With Promptrepo, you can use versioning to compare model outputs across different datasets. In the test UI, we calculate confidence scores using @promptrepo/score [1], which parses OpenAI’s logprobs and shows field-level reliability. Fields with low confidence are highlighted in red, making it easy to catch signs of overfitting or data drift.

[1] https://github.com/ManiDoraisamy/promptrepo-score

Agree, not super familiar with this space but that you are using GS as the UI makes this super accessible to so many more people given familiarity and low intimidation factor. Best of luck.

Don’t want to sound like a shill, but Google doesn’t use data from your Google Sheets to train its models or for advertising. By default, your data stays private and is protected under Google Workspace’s privacy policies.

We don’t have automated evals, latency, or cost comparisons yet. But, Promptrepo does offer versioning and lets you deploy the same model across providers for comparison. Automating these comparisons is definitely on our roadmap.

Depends on who’s using the tool. Developers might be fine coding a form into their app, but an HR person needs a form builder. Similarly, the data for training models usually lives with domain experts, but they don’t have a tool to actually do the training. That’s why, in this use case, a simple interface makes sense, IMO.

Can you please clarify what you mean by "OpenAI projects"? Are you referring to the playground or the API for prompting or fine-tuning?

Yeah, just like Dropbox was a passthrough for aws s3.

Edit: Sorry about the snide comment. But if this ends up as a simple utility for finetuning, I would be happy with that too. Just want to share a tool that's been very useful in building ai features in our products.