Large language models

Most businesses use only a fraction of what Claude can actually do. The difference between average results and real business impact is not the AI. It is the prompt. This book gives you 100 proven Claude prompts for solving high-value business problems, complete with practical guidance so you can implement them immediately and turn AI into a competitive advantage.

Advanced Prompt Engineering for LLMs: 2026 Techniques That Actually Deliver Results takes readers beyond basic instructions and introduces a complete system for working with modern Large Language Models.Discover how to:• Apply powerful frameworks such as RACE and TREE • Build advanced multi-layer prompts • Use meta-prompting to create and improve prompts • Design specialized expert personas

Build AI agents that do real work with the Google Agent Development Kit (ADK), Python, and Google Cloud. Through complete, runnable examples, you'll learn how to build, orchestrate, and deploy production-ready multi-agent systems with practical skills you can apply from day one.

Go beyond simple chatbots and build production-ready AI agents with LlamaIndex. Through practical projects and working Python code, you will learn to design reliable systems with retrieval, workflows, multi-agent architectures, observability, and deployment techniques for real-world applications.

Run powerful AI models directly in the browser with no servers and no cloud dependencies. LiteRT.js teaches you how to build fast, private, and production-ready machine learning applications using WebAssembly, WebGPU, and WebNN, with practical examples and performance-focused techniques throughout.

Discover how to build production-ready AI agents and multi-agent systems with CrewAI. Through practical examples and real-world projects, you will learn to create autonomous agents that collaborate, use tools, integrate with external data, and scale from prototype to production.

Learn how to build intelligent, production-ready AI agents with the OpenAI Agents SDK. Through practical Python examples and step by step guidance, you'll master everything from the fundamentals to advanced multi-agent workflows, tools, and deployment.

Build intelligent AI applications with LangChain using practical, production-ready Python examples. From intelligent agents and retrieval-augmented generation to scalable deployment and observability, this book equips you with the skills and architectural understanding needed to create reliable, real-world LLM-powered systems.

Move beyond the API. Dismantle the AI black box and build generative engines from scratch with pure Python and NumPy. Master the profound geometric principles and applied mathematics driving LLMs and Transformers. Transform from a mere consumer into an elite AI innovator by writing the core mathematical architecture yourself—no shortcuts, no frameworks, just pure engineering excellence.

Learn to build production-ready LLM applications with DSPy through hands-on tutorials, complete runnable examples, and real-world projects. Master DSPy's core abstractions and create AI systems that improve with data instead of endless prompt tweaking.

Learn how to build autonomous, production-ready multi-agent AI systems in C# with .NET. Using Harness, Hermes Agents, and Loop Engineering, you'll create intelligent agents that reason, collaborate, and solve real-world problems through practical examples and hands-on projects.

Build AI agents that do more than generate text. This book shows you how to use the Claude Agent SDK to create autonomous systems that use tools, manage context, coordinate multiple agents, and solve complex tasks. With practical examples in Python and TypeScript, you will learn how to build secure, scalable agents ready for production.

A practical, systems-first guide to understanding LLMs, RAG, AI agents, GraphRAG, evaluation, fine-tuning, and modern AI applications — without needing to start with equations.

Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.