Documentation and Governance

"If a dataset cannot explain where it came from, it cannot explain what it taught."

Overview

Documentation and governance convert a data pipeline into a reproducible, reviewable, and accountable artifact. In an LLM training run, data is not an inert pile of text; it is the empirical distribution that defines the examples, losses, risks, and capabilities the model will see.

This section is written as LaTeX Markdown. Inline mathematics uses $...$, and display equations use `

`. The goal is to connect data engineering decisions to mathematical objects such as records $r_i$, token sequences $x_{1:T}$, filters $f(x)$, hashes $h(x)$, mixture weights $\boldsymbol{\alpha}$, and empirical expectations.

The scope is deliberately narrow: this chapter owns the training-data pipeline. Tokenizer design, GPU training systems, benchmark methodology, alignment objectives, and production MLOps each have their own canonical chapters. Here we study the data objects that those later systems consume.

Prerequisites

• Contamination and Dedup Audits

Companion Notebooks

Learning Objectives

After completing this section, you will be able to:

• Define data cards, provenance graphs, license fields, risk registers, and release checklists
• Write dataset documentation that explains intent, sources, processing, and limitations
• Represent lineage through source URIs, hashes, transforms, and version pins
• Design governance controls for access, PII review, takedown, and release approval
• Track dataset versions and diff reports
• Link trained models back to exact data manifests
• Explain why documentation is a user-facing product, not a README afterthought
• Prepare evidence needed for reproducibility and responsible release

Study Flow

1. Read the pages in order and pause after each page to restate the main definition or theorem.
2. Run theory.ipynb when you want to check the formulas numerically.
3. Use exercises.ipynb after the reading path, not before it.
4. Return to this overview page when you need the chapter-level navigation.

Runnable Companions

• Theory notebook
• Exercises notebook