The Uncomfortable Truth Nobody Wants to Hear
At FineTune Lab, we've experimented with several different ways of creating quality datasets. Most, if not all, have failed.
Creating quality data that your model can actually learn isn't about volume or the smartest reasoning model. It's about the nuances that only you—or the people within your company—are capable of understanding.
Nobody wants to hear this. They just don't. But the reality is that a dataset's quality is going to dictate the quality of the model you decide to fine-tune.
The Pipeline Fantasy
We designed pipelines around reasoning models. The idea was simple: send in company data and Q&As, let the LLM organize everything, minimal human intervention required.
We started with DeepSeek. Relatively cheap, good reasoning skills. Should do the job, right?
It did a pretty good job. But it didn't work out the way we expected.
What Reasoning Models Excel At:
What They Can't Do:
We Didn't Learn Our Lesson
Sad to say, we didn't learn right away. We kept testing.
- DeepSeek ❌
They all do a fantastic job of giving you incredibly structured, precise, accurate data based on what you provide. But the issue is context.
They can't keep it all together at the same time.
Our First Dataset Was Trash
And man, we didn't really know it.
We used it. The model learned a few things. But here's what it couldn't do:
It couldn't say "no."
The model wasn't able to tell users what we don't do as a company. Instead, it made up information. Close enough to be believable. Close enough to sound like something a platform like FineTune Lab would offer.
The UI Navigation Nightmare
This got especially bad with UI components. The model would reference a "dashboard" we don't have. It would tell users to click buttons that don't exist.
You can't ask DeepSeek or GPT-5 Pro to understand how to navigate your specific website.
What "Properly" Actually Means
Properly means sitting down in front of each and every one of those Q&As, going over them, and making sure they say exactly what you want them to say.
That's it. That's the secret.
There's no shortcut. There's no magic prompt. There's no reasoning model smart enough to replace you actually knowing your product.
Perfect Training ≠ Working Model
We've had training runs that look perfect on paper. Loss curves trending down beautifully. Eval metrics looking great.
Then we test in the actual web portal. Garbage.
Testing is the only real way to say "hey, this works." You may have a flawless training session, but if the model didn't actually learn because:
- Data was scrambled or incorrect
...then it doesn't matter how pretty your loss curve looked.
Hundreds of Training Runs. 90% Failed.
Let's be real. Not every run teaches you something. Most of them—probably 90%—fail miserably.
Setting up training isn't difficult, especially on our platform. What's difficult is running hundreds of experiments trying to find what works.
But here's what we noticed:
The One Signal That Actually Matters
The better the data, the better the training curves.
When your data is good:
When your data is garbage:
That gap between train loss and eval loss? That's your data quality indicator. Tight gap = model is learning generalizable patterns. Wide gap = model is memorizing garbage.
We didn't learn this from a paper. We learned it from staring at hundreds of failed training runs in the analytics page, trying to figure out what the hell went wrong.
What We Actually Learned
After all the iteration, the money spent, the research:
We went from shit quality → relatively good quality → final iteration where the model learns 99% of what we're teaching it.
The GraphRAG Realization
We don't need GraphRAG for knowledge that doesn't change.
GraphRAG should be used for data that changes periodically. For stable knowledge about how your platform works? That belongs in the model itself.
Who We Are
At FineTune Lab, we're not engineers. We're not data scientists. We're not the most knowledgeable about how AI works.
But we're passionate. About the future, about technology, about helping people, about offering tools that major corporations either can't offer because it's not profitable, or refuse to offer because they're too good.
We want to get AI fine-tuning in the hands of the 16-year-old kid studying quantum mechanics, the Tony Starks who don't have money but have know-how.
We started this company for people like you.
The Bottom Line
A quality dataset has nothing to do with the largest, most powerful reasoning model.
It has everything to do with the nuances that only you—or the people within your company—are capable of answering.
Quality and balance over quantity. Always.