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A leprechaun leaned on AI To secret his gold on the sly But it drew up a map That was absolute crap And he kissed all his treasure goodbye
New MEAP! Retrieval Augmented Generation, The Seminal Papers
Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data. RAG has grown from a simple prompt engineering workflow into a sophisticated set of data analysis, storage, and retrieval techniques. Retrieval Augmented Generation, The Seminal Papers explores 12 foundational research papers that explain why RAG works, how it’s built, and what makes it different from other approaches.
This authoritative book explores the papers that define RAG’s enduring architectural pattern. Author Ben Auffarth traces RAG’s evolution from the foundational breakthroughs of REALM, RAG, and DPR to architectures like FiD and Atlas. Designed to be both interesting and practical, Retrieval Augmented Generation, The Seminal Papers illuminates techniques that empower systems to retrieve intelligently, evaluate themselves, and recover from errors. Over 40 code samples, architectural diagrams, and industry case studies make each concept easy to understand. As you master the patterns behind RAG, you’ll better understand tradeoffs, diagnose failures, and effectively evaluate and improve your own RAG implementations. [Read more]
5 chapters of this new MEAP are available now with more to follow soon!
Acclaimed ML research engineer Sebastian Raschka takes you inside the black box of reasoning-enhanced LLMs. You’ll start with a compact, pre-trained base model that runs on consumer hardware, then upgrade it step-by-step to tackle ever-more difficult problems and scenarios. You’ll measure its performance, add reasoning at inference time without training, and then improve it further with reinforcement learning. [Read more]
7 chapters of this MEAP are available now, with more to follow soon!
Written by former Google research scientist Hamza Farooq, this hands-on guide takes you from LLM and transformer fundamentals through keyword search and semantic retrieval to production RAG systems. You’ll build a hotel search engine with semantic ranking, implement semantic caching for cost-effective production deployments, develop autonomous AI agents powered by RAG context, and deploy optimized open-source LLMs. Through under-the-hood experience, you’ll master embeddings, chunking, reranking, vector databases, evaluation frameworks, fine-tuning, and more. [Read more]
6 chapters of this MEAP are available now, with more to follow soon!
Build your own DeepSeek clone from the ground up. First, you’ll quickly review LLM fundamentals, with an eye to where DeepSeek’s innovations address the common problems and limitations of standard models. Then, you’ll learn everything you need to create your own DeepSeek-inspired model, including the innovations that put DeepSeek on the map: Multihead Latent Attention (MLA), Multi-Token Prediction (MTP), Mixture of Experts (MoE), model distillation, and reasoning. [Read more]
5 chapters of this MEAP are available now, with more to follow soon!
This book shows you how to build a complete, working system of agents. Each chapter builds a new stage of your system. Begin by developing your first scratch-built agent, continue to integrate MCP compatibility, incorporate key patterns and designs like human-in-the-loop and memory, and finally implement full Agent2Agent capability that distributes a task among multiple agents. [Read more]
4 chapters of this new MEAP are available now, with more to follow soon!
Bestselling author Jungjun Hur and AI expert Younghee Song guide you through creating a complete research assistant agent framework. You’ll learn how agents function under the hood—all without hidden abstractions, black boxes, or framework lock-in. You will implement each piece as you develop a mental model of how agents really work. [Read more]
7 chapters of this new MEAP are available now, with more to follow soon!
Bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. [Read more]
After ChatGPT used RLHF to become production-ready, this foundational technique exploded in popularity. In this unique guide, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines, and their trade-offs. Using hands-on experiments and mini-implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool. [Read more]
Work within the CUDA ecosystem, from your first kernel to implementing advanced LLM features like Flash Attention. You’ll learn to profile with Nsight Compute, identify bottlenecks, and understand why each optimization works. By solving problems at multiple levels of abstraction, you’ll develop a deep understanding of CUDA, along with a practical mastery of kernel-building skills. Written for the latest NVIDIA hardware, the book builds a deep understanding of CUDA fundamentals that will stay relevant as chips upgrade and evolve. [Read more]
4 chapters of this MEAP are available now, with more to follow soon!
Whether you’re upgrading your customer service systems, creating a research assistant, or deploying a fleet of internal agents to automate enterprise tasks, this hands-on guide will help you build trustworthy agents that can handle high-stakes tasks. Extensively revised, this second edition contains new chapters on MCP, containerized deployment, and voice agent orchestration, along with refreshed coverage of LangChain, Prompt Flow, CrewAI, and more. [Read more]
5 chapters of this MEAP are available now, with more to follow soon!
Discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. You’ll learn to create KGs from first principles, go hands-on to develop advisor applications for real-world domains like healthcare and finance, build retrieval augmented generation for LLMs, and more. [Read more]
Discover a structured playbook for safely harnessing the potential of Generative AI, including security and privacy, bias, ethics, cost management, and regulation. You’ll begin with a look at common deployment scenarios and consider how those choices affect control, accountability, and risk. [Read more]
5 chapters of this MEAP are available now, with more to follow soon!
Learn to minimize the computational horsepower your models require, while keeping high–quality performance times and output. You’ll appreciate the clear explanations of complex technical concepts alongside working code samples you can run and replicate on your laptop. Plus, you’ll learn to develop and deliver RAG systems and AI agents that rely solely on SLMs, and without the costs of foundation model access. [Read more]
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