Research, experiments, and real results from the FineTune Lab team
This isn't a marketing blog. It's our lab journal. We document what we try, what works, what fails, and what we learn. Every fine-tuning technique we recommend has been tested here first.
We tried every shortcut. Reasoning models, automated pipelines, expensive APIs. All failed. Here's what actually works—and why nobody wants to hear it.
How we trained our own support model through 10+ dataset iterations, fixing reasoning one Q&A at a time. See the before/after comparisons.
How adding "wrong answer" examples and edge cases dramatically improved our model's accuracy on ambiguous questions.
Our approach to training models that reason through problems using platform-specific context instead of generic knowledge.
We ran experiments comparing dataset sizes vs quality. The results changed how we think about data curation.
Why category balance isn't enough. Learn why you need 7+ examples per fact and how to balance similar data points for robust model performance.