Practical guidance for building clean, domain-relevant datasets for fine-tuning, continued pretraining, or training from scratch
9 min readJust now
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If you’ve worked on language models beyond a quick prototype, you already know where the real bottleneck is. It’s not GPU capacity or model architecture. It’s the data.
You can replicate a model architecture in an afternoon, but if your corpus is noisy or unbalanced, all you’re doing is scaling the noise. Most teams discover this the hard way. They fine-tune endlessly, chase better prompts, and tweak loss functions — only to realize the issue isn’t in the model weights but in what the model is being taught.
High-quality data is what separates a mode…
Practical guidance for building clean, domain-relevant datasets for fine-tuning, continued pretraining, or training from scratch
9 min readJust now
–
Press enter or click to view image in full size
Source: Image by the author.
If you’ve worked on language models beyond a quick prototype, you already know where the real bottleneck is. It’s not GPU capacity or model architecture. It’s the data.
You can replicate a model architecture in an afternoon, but if your corpus is noisy or unbalanced, all you’re doing is scaling the noise. Most teams discover this the hard way. They fine-tune endlessly, chase better prompts, and tweak loss functions — only to realize the issue isn’t in the model weights but in what the model is being taught.
High-quality data is what separates a model that just runs from one that performs. Getting there means building repeatable pipelines for collection, cleaning, and structure, not relying on ad-hoc scripts and good luck.
This guide outlines a practical approach for data scientists and ML engineers who want to build reliable, domain-specific LLMs. Whether you’re fine-tuning, continuing pretraining, or training from scratch, the process comes down to the same fundamentals: clear data objectives, disciplined preparation, and…