From raw text to training gold: How to collect and prepare data for custom LLMs

Author(s): Laura Verghote

Originally published on Towards AI.

Practical guidance for building clean, domain-relevant datasets for fine-tuning, continued pretraining, or training from scratch

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.

This guide explains the critical importance of high-quality data in training effective language models, outlining practical steps for collecting, cleaning, and structuring datasets. It emphasizes different data strategies for fine-tuning, continued pretraining, or training from scratch, making clear how each approach affects model behavior and performance. The article also stresses the need for disciplined preparation and governance of data to ensure models are reliable and generalize well while detailing the significance of a clean data pipeline in minimizing compliance risks and improving overall model efficacy.

Read the full blog for free on Medium.

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Published via Towards AI