Pre-training Data and Scaling
For teams designing defensible corpora, objectives, mixtures, and compute-optimal pre-training programs.
Part of Language AI Handbook. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.
Become a more confident language AI practitioner.
Build the understanding to explain how language models work and make more informed choices about using them. The handbook connects the foundations to the methods behind modern language AI, with explanations you can return to as you learn.
See what you’ll learnWhat you will learn
- Understand Data Curation.
- Understand Data Governance, Provenance, and Synthetic Ecosystems.
- Understand Pre-training Objectives.
- Understand Scaling Laws.
Who this volume is for
- Engineers, researchers, students, AI enthusiasts, linguists, product managers, and anyone interested in understanding or building modern language AI systems, from foundational NLP to advanced large language models.
Prerequisites: Assumes Volume 5 and working knowledge of optimization.
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out of 5
12 five-star reader reviews
Genuine feedback, with names abbreviated for privacy.
Meet the community“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“Thank you Michael. It's top!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“Thanks for putting cool stuff out into the world.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Thank you Michael. It's top!”
“Thanks for putting cool stuff out into the world.”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Exact contents
24 chapters across 4 parts
Every linked chapter is free to read online. 6 further chapters are planned; unlinked titles are still in progress.
Part XX: Data Curation
- 01Web Crawling
Covers Common Crawl, crawling strategies, robots.txt respect, crawl freshness.
- 02Document Extraction
Covers HTML parsing, boilerplate removal, content extraction, trafilatura and similar tools.
- 03Language Identification
Covers language ID models, multilingual document handling, code-switching, language filtering.
- 04Deduplication
Covers exact deduplication, near-duplicate detection, document vs substring dedup, dedup at scale.
- 05MinHash
Covers MinHash algorithm, Jaccard similarity estimation, MinHash LSH, MinHash implementation.
- 06Quality Filtering
Covers heuristic filters, perplexity filtering, classifier-based filtering, filter thresholds.
- 07Toxicity Filtering
Covers toxicity classifiers, toxicity thresholds, over-filtering risks, toxicity filter evaluation.
- 08PII Removal
Covers PII detection methods, PII removal strategies, PII removal evaluation, privacy preservation.
- 09Data Mixing
Covers domain proportions, quality weighting, data mixing experiments, optimal mixing.
- 10Synthetic Data
Covers synthetic data generation, quality verification, synthetic data diversity, distillation.
Part XXI: Data Governance, Provenance, and Synthetic Ecosystems
- 01Dataset Lineage and Provenance · In progress
Tracing documents through collection, filtering, mixing, training, evaluation, and model release.
- 02Licensing, Consent, and Data-Use Signals · In progress
Licenses, terms, robots directives, creator consent, jurisdictional uncertainty, and defensible data decisions.
- 03Attribution and Training-Data Transparency · In progress
Data statements, source disclosure, influence estimation, attribution limits, and communicating uncertainty.
- 04Synthetic Data Pipelines · In progress
Generation, filtering, diversity control, verification, curriculum design, and provenance for synthetic corpora.
- 05Recursive Training and Model Collapse · In progress
Feedback loops from model-generated data, tail loss, distribution drift, and mitigations for synthetic ecosystems.
- 06Data Audits and Release Governance · In progress
Pre-training audits, sensitive-content review, documentation, approval gates, and post-release traceability.
Part XXII: Pre-training Objectives
- 01Causal Language Modeling
Covers CLM objective formulation, autoregressive factorization, CLM loss computation, CLM for generation, CLM training data, CLM scaling properties.
- 02Masked Language Modeling
Covers MLM objective formulation, masking strategies (15% rule), [MASK] token usage, MLM for understanding, MLM training dynamics.
- 03Whole Word Masking
Covers subword masking problems, whole word masking procedure, WWM implementation, WWM vs random masking, WWM for different tokenizers.
- 04Span Corruption
Covers span selection strategies, span length distribution, sentinel tokens, T5-style corruption, span corruption benefits.
- 05Prefix Language Modeling
Covers prefix LM formulation, prefix LM attention pattern, prefix LM for generation, prefix LM training, UniLM-style objectives.
- 06Replaced Token Detection
Covers generator-discriminator setup, replaced vs original detection, RTD efficiency advantages, ELECTRA training procedure, RTD vs MLM comparison.
- 07Denoising Objectives
Covers token deletion, token shuffling, sentence permutation, document rotation, BART-style denoising, combining denoising tasks.
Part XXIII: Scaling Laws
- 01Power Laws in Deep Learning
Covers power law definition, log-log linear relationships, power law fitting, power law universality, power law intuition.
- 02Kaplan Scaling Laws
Covers loss vs parameters, loss vs data, loss vs compute, Kaplan optimal allocation, Kaplan predictions.
- 03Chinchilla Scaling Laws
Covers Chinchilla experiments, revised scaling coefficients, optimal tokens per parameter, Chinchilla vs Kaplan, Chinchilla implications.
- 04Compute-Optimal Training
Covers compute budget allocation, tokens vs parameters ratio, training efficiency, compute-optimal recipes, practical guidelines.
- 05Data-Constrained Scaling
Covers data repetition effects, optimal repetition strategies, data augmentation scaling, synthetic data scaling.
- 06Inference Scaling
Covers training vs inference compute, inference-optimal models, over-training for efficiency, deployment cost modeling.
- 07Predicting Model Performance
Covers loss extrapolation, capability prediction, scaling law uncertainty, prediction reliability, practical forecasting.
Choose the edition that fits your next step
Volume 7: Pre-training Data and Scaling
760 pages focused on this subject, with future updates included.
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Get all-volume accessExplore the other volumes
Volume 1 · 570 pages
Language Data and Classical NLP
Text as Data · Classical Text Representations · Distributional Semantics
Volume 2 · 790 pages
Embeddings, Tokenization, and Sequence Labeling
Word Embeddings · Subword Tokenization · Sequence Labeling
Volume 3 · Coming soon
Linguistic Structure and Meaning
Morphology · Syntax · Semantics · Discourse · Dialogue
Volume 4 · 920 pages
Neural NLP and Sequence Models
Neural Network Foundations · Recurrent Neural Networks · Sequence-to-Sequence
Volume 5 · 860 pages
Transformers from First Principles
Self-Attention · Positional Encoding · Transformer Blocks · Transformer Architectures
Volume 6 · 510 pages
Efficient, Long-Context, and Alternative Architectures
Efficient Attention · Long Context
Volume 8 · 440 pages
Encoder, Multilingual, and Translation Models
BERT and Variants · Encoder-Decoder Models
Volume 9 · 730 pages
Decoder Language Models and Capabilities
GPT Architecture · Modern Decoder Models · Emergent Capabilities
Volume 10 · 920 pages
Large-Scale Training and Sparse Models
Training Infrastructure · Training Optimization · Mixture of Experts
Volume 11 · 730 pages
Fine-tuning and Efficient Adaptation
Fine-tuning Fundamentals · Parameter-Efficient Fine-tuning · Instruction Tuning
Volume 12 · 510 pages
Alignment, Preferences, and Oversight
Alignment and RLHF
Volume 13 · 220 pages
Reasoning and Verifiable Learning
Reasoning
Volume 14 · 640 pages
Efficient Inference and On-Device Models
Model Compression · Inference Optimization
Volume 15 · 600 pages
Retrieval, Knowledge, and Memory
Retrieval-Augmented Generation · Continual Learning
Volume 16 · 510 pages
Agents and AI Coding Systems
Code Generation · Tool Use and Agents
Volume 17 · 540 pages
Speech, Multimodal, and Embodied Language
Multimodal Models · Speech and Audio
Volume 18 · 950 pages
Evaluation, Factuality, and Reliability
Evaluation Fundamentals · Benchmark Evaluation · Human and Model Evaluation · Hallucination and Factuality
Volume 19 · 760 pages
Responsible, Secure, and Interpretable Language AI
Bias and Fairness · Interpretability · Safety and Security
Volume 20 · 510 pages
Applications, Production, and Compound AI Systems
LLM Applications · Production Systems
Author and edition details
About the author and this edition
Michael Brenndoerfer
Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.
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- First edition
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- Last reviewed
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Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
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Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.