Large Language Models (LLMs) are transformer-based deep learning architectures designed to process and generate human language at scale.
These models are trained on extensive tokenized text corpora using unsupervised and self-supervised learning techniques. Their ability to predict sequences and understand context has made them essential to modern artificial intelligence.
LLMs power a wide range of applications in natural language processing, including machine translation, conversational AI, summarization, sentiment analysis, and code generation.
Model families like GPT, LLaMA, Claude, and PaLM exemplify advancements in parameter scaling, multi-modal processing, and zero-shot learning.
LLMs are also critical to the development of AI agents, prompt-based systems, and domain-adapted large-scale inference engines.
Understanding their architecture requires familiarity with concepts like attention mechanisms, positional encoding, fine-tuning, and inference optimization.
Best LLMs Books
The best LLMs books provide a structured path into these topics, combining model theory, deployment workflows, ethical considerations, and real-world applications.
Build a Large Language Model (From Scratch)
- Raschka, Sebastian (Author)
- English (Publication Language)
- 368 Pages - 10/29/2024 (Publication Date) - Manning (Publisher)
Hands-On Large Language Models: Language Understanding and Generation
- Alammar, Jay (Author)
- English (Publication Language)
- 425 Pages - 10/15/2024 (Publication Date) - O'Reilly Media (Publisher)
AI Engineering: Building Applications with Foundation Models
- Huyen, Chip (Author)
- English (Publication Language)
- 532 Pages - 01/07/2025 (Publication Date) - O'Reilly Media (Publisher)
The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)
- Burkov, Andriy (Author)
- English (Publication Language)
- 156 Pages - 01/15/2025 (Publication Date) - True Positive Inc. (Publisher)
LLM Engineer’s Handbook: Master the art of engineering large language models from concept to production
- Iusztin, Paul (Author)
- English (Publication Language)
- 522 Pages - 10/22/2024 (Publication Date) - Packt Publishing (Publisher)
Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications
- Berryman, John (Author)
- English (Publication Language)
- 280 Pages - 12/31/2024 (Publication Date) - O'Reilly Media (Publisher)
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
- Raieli, Salvatore (Author)
- English (Publication Language)
- 560 Pages - 07/11/2025 (Publication Date) - Packt Publishing (Publisher)
The Agentic AI Bible: The Complete and Up-to-Date Guide to Design, Build, and Scale Goal-Driven, LLM-Powered Agents that Think, Execute and Evolve
- Caldwell, Thomas R. (Author)
- English (Publication Language)
- 459 Pages - 08/03/2025 (Publication Date) - Independently published (Publisher)
Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
- Bouchard, Louis-François (Author)
- English (Publication Language)
- 463 Pages - 05/21/2024 (Publication Date) - Independently published (Publisher)
Juan Farin: An LLM Awakens
- Amazon Kindle Edition
- Sipper, Moshe (Author)
- English (Publication Language)
- 103 Pages - 09/04/2025 (Publication Date)
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
- Phoenix, James (Author)
- English (Publication Language)
- 422 Pages - 06/25/2024 (Publication Date) - O'Reilly Media (Publisher)
Understanding LLMs: A Short Technical Dive (The AI Essentials)
- Amazon Kindle Edition
- Langford, Lex (Author)
- English (Publication Language)
- 28 Pages - 06/22/2025 (Publication Date)
Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
- Poisson, Peter E. (Author)
- English (Publication Language)
- 163 Pages - 07/26/2025 (Publication Date) - Independently published (Publisher)
The Most Complete AI Agentic Engineering System: Step-by-step guide to build, optimize, and scale LLM agents—with exclusive monthly and rigorous ... metrics, and built-in self-improvement
- Raynor, Christopher (Author)
- English (Publication Language)
- 369 Pages - 10/01/2025 (Publication Date) - Independently published (Publisher)
LLMs in Production: From language models to successful products
- Brousseau, Christopher (Author)
- English (Publication Language)
- 456 Pages - 02/11/2025 (Publication Date) - Manning (Publisher)
Python, Deep Learning, and LLMs: A Crash Course for Complete Beginners
- Tkachenko, Yegor (Author)
- English (Publication Language)
- 448 Pages - 09/03/2025 (Publication Date) - Yegor Tkachenko (Publisher)
Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph
- Auffarth, Ben (Author)
- English (Publication Language)
- 480 Pages - 05/23/2025 (Publication Date) - Packt Publishing (Publisher)
Data Analysis with LLMs: Text, tables, images and sound (In Action)
- Trummer, Immanuel (Author)
- English (Publication Language)
- 232 Pages - 05/27/2025 (Publication Date) - Manning (Publisher)
Mastering the Data Paradox: Key to Winning in the AI Age
- Seth, Nitin (Author)
- English (Publication Language)
- 584 Pages - 04/10/2024 (Publication Date) - Penguin Random House India Pvt. Ltd (Publisher)
Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph
- Oshin, Mayo (Author)
- English (Publication Language)
- 294 Pages - 03/25/2025 (Publication Date) - O'Reilly Media (Publisher)
LLMs represent a major leap in artificial intelligence, shifting machine learning paradigms toward general-purpose models that can adapt across multiple tasks without explicit retraining.
They are central to developments in foundation models, retrieval-augmented generation (RAG), and few-shot or in-context learning techniques.
Working with LLMs involves knowledge of prompt engineering, scalable infrastructure, memory optimization, token limits, and output evaluation strategies.
Ethical considerations such as model bias, hallucination, alignment, and explainability are also essential components of responsible LLM deployment.
The best LLMs books offer in-depth insights into both theoretical foundations and production-level practices, building topical authority across language modeling, system design, and AI safety.
Whether you’re researching model interpretability or deploying generative agents at scale, gaining mastery of LLMs is now indispensable for engaging with the evolving AI landscape.
