Context Engineering
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Context Engineering for Multi-Agent Systems
Author: Denis Rothman
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-11-18
Build AI that thinks in context using semantic blueprints, multi-agent orchestration, memory, RAG pipelines, and safeguards to create your own Context Engine Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Design semantic blueprints to give AI structured, goal-driven contextual awareness Orchestrate multi-agent workflows with MCP for adaptable, context-rich reasoning Engineer a glass-box Context Engine with high-fidelity RAG, trust, and safeguards Book DescriptionGenerative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios. Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence. By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence. *Email sign-up and proof of purchase requiredWhat you will learn Develop memory models to retain short-term and cross-session context Craft semantic blueprints and drive multi-agent orchestration with MCP Implement high-fidelity RAG pipelines with verifiable citations Apply safeguards against prompt injection and data poisoning Enforce moderation and policy-driven control in AI workflows Repurpose the Context Engine across legal, marketing, and beyond Deploy a scalable, observable Context Engine in production Who this book is for This book is for AI engineers, software developers, system architects, and data scientists who want to move beyond ad hoc prompting and learn how to design structured, transparent, and context-aware AI systems. It will also appeal to ML engineers and solutions architects with basic familiarity with LLMs who are eager to understand how to orchestrate agents, integrate memory and retrieval, and enforce safeguards.
Context Engineering for AI
Author: Jacobs V Bradley
language: en
Publisher: Independently Published
Release Date: 2025-08
Master the Future of AI with Context Engineering - Transform Prompts into Production-Ready Intelligence In a world where AI is powering business, research, and innovation, simply knowing how to write prompts is no longer enough. Context engineering is the missing link that separates hobbyist experiments from scalable, production-ready AI systems. This book, "Context Engineering for AI: From Prompting to Production," gives you the complete blueprint to design reliable, cost-efficient, and high-performing LLM applications that thrive in real-world environments. Inside, you will learn how to structure context pipelines, implement Retrieval-Augmented Generation (RAG), optimize tokens for cost and speed, and add short-term and persistent memory for multi-turn conversations. Through hands-on projects, real-world case studies, and production-proven techniques, you'll gain the practical skills to transform AI from a concept into a business-ready solution. Written by Jacobs V. Bradley, a seasoned technology expert and thought leader in AI systems and intelligent automation, this book reflects up-to-date industry trends and provides actionable insights for developers, data scientists, AI engineers, and technology leaders. Whether you're building document intelligence for finance, customer support automation at scale, or multi-agent context routing systems, this guide empowers you to engineer AI solutions that are accurate, reliable, and future-proof. If you want to go beyond prompt engineering and gain the technical mastery that modern enterprises demand, this is the essential guide. Future-proof your skills, boost your AI expertise, and turn concepts into deployable, revenue-generating systems with context engineering. Perfect For: Developers and AI engineers building real-world LLM applications Tech leaders seeking scalable, cost-efficient AI solutions Readers of top-selling AI, machine learning, and automation books
The Context Engineering Handbook
Author: NEWMAN. CHANDLER
language: en
Publisher: Independently Published
Release Date: 2025-07-22
The Context Engineering Handbook: Build reliable, high-performance LLM systems using Scalable RAG Architectures with LlamaIndex and Vector Databases Struggling to keep your AI agents accurate and scalable in a world of exploding data and token limits? The Context Engineering Handbook offers a step-by-step blueprint for mastering context engineering-the art of building reliable, high-performance LLM systems using Scalable RAG architectures, LlamaIndex, and modern vector databases. You'll move beyond proof-of-concept prompts into production-grade pipelines that deliver consistent, cost-effective results. What's inside: Discover how to architect end-to-end retrieval-augmented generation (RAG) workflows that: Ingest and chunk documents with precision Embed and store vectors in Pinecone, Weaviate, or Qdrant Design dynamic prompt pipelines using LangChain and LlamaIndex Implement real-time streaming ingestion for live updates Manage memory with hierarchical summaries and scratchpads Secure inputs, redact PII, and maintain audit-ready logs You'll gain: Practical skills to build scalable RAG architectures that serve thousands of requests per second Hands-on expertise with LlamaIndex data structures and vector database integrations Proven strategies for cost-monitoring, KV-cache optimization, and token-budget management Techniques to isolate context in sub-agents and orchestrate complex workflows with Orkes Conductor or LangGraph Security and compliance best practices, from input sanitization to immutable audit trails Advanced methods like reinforcement-learning-driven context selection and self-validation testing Take the next step: Add The Context Engineering Handbook to your toolkit today-your AI systems will thank you.
