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Fig. I — A run, in order

Data Engineering

4 parts, read in order — each one assumes the one before it. Back toall runs orthe full archive.

Start at the beginning

The parts

When I started learning ML, I assumed the hard part was the model — architectures, optimizers, all the math I've written about so far.

  1. From Raw Web to Training Corpus: The Unglamorous Pipeline Behind Every LLM
  2. Quality vs. Quantity: The Data Question That Keeps Changing Its Answer
  3. Synthetic Data: Training Models on Text That Models Wrote
  4. Building a Fine-Tuning Dataset: The Practical Craft Nobody Writes Papers About