Context engineering can be defined as the practice of designing systems that provide a Large Language Model (LLM) and AI agents with all the necessary information to complete a task effectively. It goes beyond prompt engineering since it focuses on building a comprehensive and structured context from various sources like instructions, external knowledge, memory, tools, and state. The central idea is that the success of a complex LLM-based system depends more on the quality and completeness of the context provided than on the specific wording of the prompt itself.
Context Engineering: Build Consistent, Accurate, Predictable AI Systems
2026-11-13
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Tobi Lütke, the CEO of Shopify, coined the term context engineering in a tweet on June 19, 2025. He defined context engineering as the art of providing all the context for the task to be plausibly solvable by the LLM. This novel concept captures the essence of the current evolution of LLM-based systems, inspiring others (like me) to understand and define this emerging discipline. Since then, I've been working on a book entitled Context Engineering: Build Consistent, Accurate, Predictable AI Systems, published by Manning. Currently, the early access version of the book is available, and you can get it here.
This GitHub repository is intended to be a companion resource for this book and a reference for practitioners looking to understand and adopt the context engineering principles.
Table of contents
This book aims to provide a strong, general-purpose theoretical foundation for context engineering, supported by hands-on examples. Its table of contents is the following:
Introduction to context engineering
Instructions for AI agents
External knowledge and retrieval
Tools in AI agents
Memory and state in agentic systems
User prompts for LLMs
Context management and orchestration
Evaluation and observability
Governance and operations
AI frameworks for context engineering
Context engineering for software development
The state of the art in context engineering
Appendix A. The AI ecosystem
Appendix B. References and further reading
Each chapter of this book begins by explaining the underlying principles and patterns of each thematic block. The final part of each chapter then presents specific examples, all of which are available in this GitHub repository. New examples will continue to be added, and existing ones maintained, even after the book is published. The goal is to provide an open-source, up-to-date reference for everyone interested in context engineering.
Examples
This repository organizes examples by chapter to help you explore context engineering concepts in practice.
Chapter 1. Introduction to context engineering
This chapter provides the foundations for interacting with different model providers:
This chapter covers the definition and usage of instructions (system prompts, agent skills, instructions artifacts) as a foundation layer to shape the model behavior:
This chapter focuses on the design and optimization of user prompts, including techniques like few-shot prompting, prompt chaining, Chain-of-Thought, or ReAct:
This chapter focuses on managing and orchestrating context in complex agentic systems, including context compression, hierarchical context, or multi-agent patterns:
This chapter covers specific AI frameworks that facilitate context engineering, including application frameworks, agent orchestration frameworks, and AI application platforms:
Chapter 11. Context engineering for software development
This chapter shows how context engineering supports the software development lifecycle (SDLC) through reusable skills, instruction artifacts, external documentation retrieval, orchestration layers, and specialized agents:
Chapter 12. The state of the art in context engineering
This chapter covers mathematical foundations, open research challenges, technical innovation frontiers, domain-specific applications, and future directions:
Browse a curated library of prompts organized around the software development lifecycle (SDLC), including roles for architect, developer, debugger, reviewer, refactorer, tester, and documenter:
Explore prompts visually using a card-based interface
Filter by framework (10-step, COSTAR, CRISPE, RTF)
Inspect full structured prompts for each role
Copy prompts directly for reuse or adaptation
This tool is designed as a reference library, helping you understand how structured prompts vary across roles and frameworks.
Searchable catalog of articles, books, repositories, tutorials, and other references related to context engineering, prompting, agents, and AI engineering: context engineering, AI agents, machine learning, prompt engineering, RAG, generative AI, MCP, AI-assisted development, LLMs, memory, retrieval, multi-agent systems.
Contributing
If you think something should be improved or want to contribute to this repo, please open a pull request. Any comments or feedback are welcome.
