Context Engineering

Has visto a un modelo de lenguaje hacer media hora de trabajo por su cuenta. Este libro trata de la pregunta que vino después: ¿hasta qué punto puedes confiar en él?

Meta Muse changes the way you work with AI. This practical guide shows you how to write better prompts, build reliable workflows, automate everyday tasks and avoid common mistakes. Whether you're new to agentic AI or already putting it to work, you'll learn how to get more done with less friction and more confidence.

You watched a language model do half an hour of work on its own. This book is about the question that came next: how much of it can you trust?

Your AI agent can sound right and still act wrong. Learn how to secure the path from a model’s suggestion to a real-world action—with practical guidance on data, permissions, tools, adversarial testing, and recovery.

Get more from Claude Sonnet 5.5 with practical prompting techniques that actually work. This guide explores instruction design, effort control, tool use, structured outputs and agentic workflows, with clear distinctions between documented behavior and practical experience. Built for developers, prompt engineers and advanced users.

What if you could build an AI that writes just like you? In Building Your LLM Twin, you'll follow a hands-on journey from raw writing data to a fully deployed AI that captures your unique voice. Learn to build, fine-tune and scale real-world LLM systems with practical code, proven techniques and the tools to take your project from concept to production.

Unlock the full potential of large language models with RAG. This book takes you from core concepts to production-ready systems, exploring everything from embeddings and search to evaluation and security. With clear explanations, runnable code and practical insights, you'll learn how to build reliable AI systems grounded in the knowledge that matters.

Build reliable, production-ready search systems with embeddings and neural reranking. Learn how to turn semantic meaning into relevant results, refine retrieved content and deliver better context for AI-generated answers. With hands-on Python examples and practical architectures, this book takes you from core concepts to real-world RAG systems.

AI can decide. That does not mean it is authorized to act. Governance Before Decision examines the missing control layer between intelligent systems and real-world consequences — where authority, scope, verification, and execution must meet.

Master AI/ML System Design with a Structured Interview FrameworkLearn how to approach complex ML system design interviews using a 20-step framework, architecture blueprints, real-world case studies, scalability strategies, data and model pipelines, deployment patterns, monitoring, and interview-ready templates.

Go beyond basic prompting and learn how to get reliable, high-quality results from Claude Opus 5.5. This practical guide shows you how to structure instructions, manage context, break down complex work and verify outputs across coding, research, analysis, writing and automation.

GPT-6 Astra changes the way prompting works. This practical guide explains what makes it different, why old techniques can fail and how to write prompts that deliver reliable results. Learn how to build, refine and troubleshoot prompts while finding the right balance between control and autonomy.

Agentic AI is everywhere, but what does it actually mean? This book cuts through the hype and explains how AI agents work, how they are built and where they can go wrong. Starting from the basics, it gives you a clear, practical understanding of the technology and what it takes to trust an agent in the real world.

Stop guessing at prompts. This visual workbook gives you 50 ready-to-run workflows, command cheatsheets, and debugging checklists for Claude Code, Cursor, and GitHub Copilot — so you can open any page and immediately know what to type.

AI can sound certain even when it is completely wrong. This book takes a practical approach to building RAG systems and AI agents that ground answers in evidence, verify what they generate and know when to stop. Learn how to make AI more reliable in the places where getting it wrong really matters.