Prompt Engineering

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.

What does it take to turn an AI agent from a clever demo into something you can trust in production? This hands-on guide uses DeepSeek Harness to show how to build reliable, secure and observable agents, from a simple script to a production-ready service with budgets, tracing, evals and multi-tenant support.

Find your next useful Claude prompt in a library of 1,000 templates across 20 domains. Customize clear instructions for writing, business, development, research and everyday work—with practical guidance for checking the results.

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.

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.

Unlock the full potential of Claude Fable 5.1 with practical prompting techniques built for real-world work. Learn how to get better results from coding, research and complex workflows, avoid common mistakes and build reliable AI systems with proven strategies and ready-to-use prompts.

What happens when AI agents stop working alone and start working as a team?Artificial Intelligence is entering a new phase in which intelligent systems can do more than respond to individual instructions. Multiple specialized agents can collaborate, divide complex tasks, communicate with one another, use tools, evaluate results, and coordinate their actions toward a shared objective.Multi-Agent AI Systems: The Complete Handbook for Building Intelligent, Scalable, and Autonomous Agent Teams provides a practical roadmap for understanding this emerging paradigm.The book begins with the fundamentals of multi-agent systems and explains why collaboration between specialized agents can be valuable for complex workflows. Readers will learn about hierarchical, peer-to-peer, and hybrid architectures, along with roles such as manager, planner, worker, critic, and supervisor agents.It then moves into modern frameworks and technologies, including CrewAI, AutoGen, LangGraph, MetaGPT, LLMs, vector databases, and agent memory systems. Practical chapters explain how to design agent teams, decompose tasks, establish communication protocols, manage shared memory, coordinate workflows, integrate tools and APIs, and recover from failures.Readers will also explore advanced concepts such as dynamic replanning, parallel execution, swarm intelligence, agent debates, self-organizing systems, human-in-the-loop workflows, and multimodal agents.The book goes beyond experimentation and addresses the challenges of deploying multi-agent systems in real environments. Cloud deployment, Docker, Kubernetes, monitoring, logging, scaling, evaluation, benchmarking, testing, and cost optimization are included.Real-world applications demonstrate how agent teams can support software development, research, customer service, content creation, and business operations.Equally important, the book examines AI safety, privacy, security, transparency, governance, alignment, and responsible AI development.Whether you are a student discovering agentic AI, a developer building your first agent team, a researcher exploring collaborative intelligence, or a professional preparing for the next generation of AI applications, this book provides a foundation for moving from individual AI agents toward coordinated intelligent systems.Understand the architecture. Design the team. Build the agents. Coordinate the intelligence.

The first thing many people hit with an AI coding assistant is a plateau. A correction made on Monday is gone by Thursday. This book is about the operating model above the prompt, where truth lives, how a correction becomes a standing rule that holds, and which judgments stay in human hands.

Master LTX 2.5 from the first prompt to the final shot. Learn to create cinematic video with synchronized audio, precise camera movement and consistent characters while avoiding the mistakes that waste time and generations. Built on official documentation and verified behavior, this is a practical guide to getting production-ready results from LTX 2.5.

Learn C, Python, and assembly — not just the syntax, but the thinking behind them. This book helps you understand computers, write real code, and see where technology meets the real world.

A practical, code first guide to building reliable, safe, and cost effective LLM systems in 2026. Learn how modern models really read your prompts, how to orchestrate tool using agents, and how to harden your systems against prompt injection, all with runnable examples across the major AI APIs.

Unlock the full potential of Qwen 3.8 with a practical guide built for real results. Learn the techniques behind stronger prompts, avoid common mistakes, and create reliable outputs for coding, research, business, creative projects and AI agents with clear examples you can use right away.

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (382 manuscript pages).

Move beyond individual prompts and engineer the full information environment an AI model receives. Learn to design, test, measure, and govern context for reliable AI workflows.