Retrieval, Knowledge, and Memory
For teams building grounded, updateable, personalized systems with retrieval, editing, memory, continual learning, and unlearning.
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 Retrieval-Augmented Generation.
- Understand Knowledge, Memory, Editing, and Unlearning.
- Understand Continual Learning.
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 embeddings from Volume 2 and model fundamentals from Volumes 8–9.
Volume 15 PDF edition
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Get all-volume access · $199Reader verdict
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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
19 chapters across 3 parts
Every linked chapter is free to read online. 6 further chapters are planned; unlinked titles are still in progress.
Part XLIV: Retrieval-Augmented Generation
- 01RAG Motivation: Solving Hallucinations & Knowledge Gaps
Discover why LLMs need Retrieval-Augmented Generation. Learn how RAG bridges knowledge gaps, reduces hallucinations, and enables non-parametric memory.
- 02RAG Architecture: Components, Timing & Design Patterns
Master RAG system design by exploring retriever-generator interactions, timing strategies like iterative retrieval, and architectural variations like RETRO.
- 03Dense Retrieval: Semantic Search & Bi-Encoder Implementation
Master dense retrieval for semantic search. Explore bi-encoder architectures, embedding metrics, and contrastive learning to overcome keyword limitations.
- 04Contrastive Learning for Retrieval: InfoNCE & DPR Guide
Master contrastive learning for dense retrieval. Learn to train models using InfoNCE loss, in-batch negatives, and hard negative mining strategies effectively.
- 05Document Chunking: Optimizing RAG Retrieval Pipelines
Master document chunking for RAG systems. Explore fixed-size, recursive, and semantic strategies to balance retrieval precision with context window limits.
- 06Embedding Models: Architecture, Pooling & Selection
Learn how embedding models convert text to vectors for RAG. Covers bi-encoder architecture, pooling strategies, dimensionality trade-offs, and model selection.
- 07Vector Similarity Search: Metrics & Approximate Methods
Explore vector similarity search for RAG systems. Compare cosine, dot product, and Euclidean metrics, and implement exact vs. approximate search with FAISS.
- 08HNSW Index: Architecture for Fast Vector Search
Master Hierarchical Navigable Small World (HNSW) graphs for vector search. Learn graph architecture, construction, and tuning for high-speed retrieval.
- 09IVF Index: Clustering-Based Vector Search & Partitioning
Master IVF indexes for scalable vector search. Learn clustering-based partitioning, nprobe tuning, and IVF-PQ compression for billion-scale retrieval.
- 10Product Quantization: Vector Compression for ANN Search
Learn how Product Quantization compresses embeddings up to 100x using learned codebooks and asymmetric distance computation for scalable vector search.
- 11Hybrid Search: BM25 and Dense Retrieval Combined
Learn how hybrid search fuses BM25 keyword retrieval with dense vector retrieval using reciprocal rank fusion and weighted score combination to improve recall.
- 12Reranking: Cross-Encoders for Precise Information Retrieval
Learn how reranking with cross-encoders solves bi-encoder limitations. Master two-stage retrieval, training strategies, and latency optimization for production search systems.
- 13RAG Prompt Engineering: Context Placement & Citation Strategies
Master RAG prompt engineering with strategic context placement, citation formats, and truncation strategies to improve LLM accuracy and reduce hallucinations.
- 14RAG Evaluation: Metrics for Retrieval and Generation Quality
Master RAG evaluation with metrics for retrieval quality (Precision@K, NDCG, MRR) and generation faithfulness using the RAGAS framework for AI systems.
Part XLV: Knowledge, Memory, Editing, and Unlearning
- 01Parametric and External Knowledge · In progress
What models store, what retrieval stores, and how to choose among prompting, RAG, editing, and retraining.
- 02Knowledge Editing · In progress
Localized weight updates, memory-based editors, specificity, generalization, and multi-hop consistency.
- 03Knowledge Freshness and Temporal Updates · In progress
Time-sensitive facts, temporal benchmarks, update propagation, versioning, and rollback.
- 04Personalization and Long-Term Memory · In progress
User models, episodic and semantic memory, consent, retention, conflict resolution, and forgetting.
- 05Machine Unlearning · In progress
Forget sets, retraining baselines, approximate removal, privacy goals, and the limits of verification.
- 06Evaluating Knowledge Interventions · In progress
Efficacy, locality, generalization, side effects, privacy leakage, and auditable change histories.
Part XLVI: Continual Learning
- 01Continual Learning Problem
Covers continual learning definition, catastrophic forgetting, continual learning scenarios.
- 02Regularization Methods
Covers elastic weight consolidation, synaptic intelligence, parameter importance, regularization trade-offs.
- 03Replay Methods
Covers replay buffer design, pseudo-rehearsal, generative replay, replay selection.
- 04Architecture Methods
Covers progressive networks, expert expansion, architecture search, modular approaches.
- 05Continual Learning Evaluation
Covers forward transfer, backward transfer, evaluation protocols, continual benchmarks.
Choose the edition that fits your next step
Volume 15: Retrieval, Knowledge, and Memory
600 pages focused on this subject, with future updates included.
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All-volume access with separate volume PDFs and future updates. No combined PDF. Includes future volumes as they become available.
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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 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 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.