Evaluation, Factuality, and Reliability
For researchers and product teams designing credible benchmarks, judges, human studies, and reliability 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 Evaluation Fundamentals.
- Understand Benchmark Evaluation.
- Understand Human and Model Evaluation.
- Understand Hallucination and Factuality.
- Understand Dynamic, Interactive, and Agentic Evaluation.
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: Can be read after any model-focused volume; basic statistics are helpful.
Volume 18 PDF edition
Own this 950-page edition
Keep the PDF for offline reading, with future updates included. The corresponding chapters stay free online.
- Carefully typeset PDF
- Future updates and errata included
- Online chapters remain free to read
Volume 18 of 20 · In progress
$24
one-timeVersion 2026.08.0 · 950 pages · secure checkout via Stripe
Delivered by email · Free updates included
Want the complete library?
Access to every volume, delivered as separate PDFs. Save $257 vs. buying available volumes separately.
Get all-volume access · $199Reader verdict
5.0
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
30 chapters across 5 parts
Every linked chapter is free to read online. 6 further chapters are planned; unlinked titles are still in progress.
Part LIII: Evaluation Fundamentals
- 01Perplexity Evaluation
Covers perplexity calculation, perplexity interpretation, perplexity limitations, comparing perplexities.
- 02Cross-Entropy Loss
Covers cross-entropy definition, bits-per-character, cross-entropy vs perplexity, loss curves.
- 03BLEU Score
Covers n-gram precision, brevity penalty, BLEU formula, BLEU limitations, corpus vs sentence BLEU.
- 04ROUGE Scores
Covers ROUGE-N, ROUGE-L, ROUGE-W, ROUGE interpretation, ROUGE limitations.
- 05BERTScore
Covers BERTScore computation, token alignment, BERTScore variants, BERTScore vs BLEU.
- 06Exact Match and F1
Covers exact match scoring, token-level F1, normalization for matching, metric selection.
- 07Calibration
Covers calibration definition, expected calibration error, calibration plots, calibration methods.
- 08Evaluation Fundamentals
Covers evaluation design principles, metric selection, evaluation pitfalls, evaluation frameworks.
- 09Benchmark Design
Covers benchmark construction, dataset collection, annotation guidelines, benchmark validity.
- 10Evaluation Challenges
Covers benchmark contamination, evaluation brittleness, gaming metrics, evaluation best practices.
Part LIV: Benchmark Evaluation
- 01MMLU
Covers MMLU structure, subject coverage, MMLU evaluation protocol, MMLU limitations.
- 02HellaSwag
Covers HellaSwag task design, adversarial filtering, HellaSwag evaluation, HellaSwag saturation.
- 03GSM8K
Covers GSM8K problem types, chain-of-thought evaluation, GSM8K accuracy metrics, math reasoning assessment.
- 04HumanEval
Covers HumanEval structure, functional correctness, pass@k metric, HumanEval limitations.
- 05MBPP
Covers MBPP dataset, MBPP vs HumanEval, code evaluation challenges.
- 06TruthfulQA
Covers TruthfulQA design, truthfulness vs informativeness, TruthfulQA evaluation methods.
- 07Benchmark Contamination
Covers contamination problem, contamination detection methods, n-gram overlap analysis, contamination mitigation.
- 08Benchmark Saturation
Covers ceiling effects, benchmark retirement, dynamic benchmarks, benchmark evolution.
Part LV: Human and Model Evaluation
- 01Human Evaluation Design
Covers evaluation interface design, task instructions, annotator selection, evaluation cost.
- 02Inter-Annotator Agreement
Covers Cohen's kappa, Fleiss' kappa, Krippendorff's alpha, handling disagreement.
- 03Preference Evaluation
Covers A/B comparison design, Elo rating systems, preference aggregation, statistical significance.
- 04LLM-as-Judge: Scalable AI Evaluation with Language Models
Learn how to build LLM-as-Judge evaluation pipelines: prompt design, judge model selection, calibration against human annotations, and bias mitigation.
- 05Position Bias in LLM Judges
Covers position bias measurement, bias mitigation (swapping), verbosity bias, sycophancy.
- 06Evaluation Prompt Engineering: Designing Reliable LLM Judges
Learn how to design reliable LLM judge prompts using explicit criteria, few-shot examples, and chain-of-thought formatting to maximize evaluation accuracy.
Part LVI: Hallucination and Factuality
- 01Hallucination Types in Language Models: A Complete Guide
Learn how language models hallucinate: intrinsic and extrinsic hallucination, factual errors, fabrication, and inconsistency with NLI-based detection.
- 02Hallucination Detection: NLI, Self-Consistency & Learned Models
Learn four methods for detecting LLM hallucinations: entailment-based scoring, knowledge base verification, self-consistency checks, and learned detection models.
- 03Hallucination Causes
Covers training data issues, exposure bias, knowledge gaps, generation pressure.
- 04Hallucination Mitigation: RAG, Decoding, and Training
Learn how to reduce LLM hallucination using retrieval augmentation, self-consistency decoding, DPO training, and calibrated uncertainty expression.
- 05Attribution and Citation: Sourcing LLM Outputs
Learn how language models link generated claims to source documents, evaluate citation accuracy with NLI, and measure attribution precision and recall.
- 06Uncertainty Quantification
Covers confidence calibration, verbalized uncertainty, sampling-based uncertainty, uncertainty communication.
Part LVII: Dynamic, Interactive, and Agentic Evaluation
- 01Live and Continuously Updated Benchmarks · In progress
Fresh questions, private test sets, rolling releases, objective scoring, and contamination controls.
- 02Evaluation Harnesses and Reproducibility · In progress
Versioned prompts, model settings, graders, confidence intervals, artifacts, and comparable reports.
- 03Interactive and Long-Horizon Task Evaluation · In progress
Stateful environments, functional outcomes, partial credit, recovery, efficiency, and trajectory analysis.
- 04Judge Calibration and Meta-Evaluation · In progress
Bias, self-preference, verbosity effects, rubric validity, judge ensembles, and agreement with experts.
- 05Online Experiments and Production Evaluation · In progress
Shadow traffic, interleaving, A/B tests, guardrail metrics, drift, and linking offline scores to user outcomes.
- 06Evaluation Governance · In progress
Benchmark access, disclosure, privacy, gaming, deprecation, audit trails, and responsible leaderboard use.
Choose the edition that fits your next step
Volume 18: Evaluation, Factuality, and Reliability
950 pages focused on this subject, with future updates included.
$24
Choose this volumeThe complete Language AI Handbook library
All-volume access with separate volume PDFs and future updates. No combined PDF. Includes future volumes as they become available.
$199
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 7 · 760 pages
Pre-training Data and Scaling
Data Curation · Pre-training Objectives · Scaling Laws
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 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.
- Edition
- First edition
- Published
- Last reviewed
Version history
Kept current, not frozen in time
Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
Current release
Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.