This repository provides a template and methodology for "Context Engineering," a systematic approach to providing AI coding assistants with comprehensive project context, aiming for more reliable and complex feature implementation than traditional prompt engineering. It is targeted at developers seeking to maximize the effectiveness of AI coding partners like Claude Code.

How It Works

The core of this approach involves structuring project context into specific files: CLAUDE.md for global rules, INITIAL.md for feature requests, and the examples/ directory for code patterns. A custom command, /generate-prp, analyzes these inputs and the codebase to create a detailed "Product Requirements Prompt" (PRP). This PRP then serves as a blueprint for another command, /execute-prp, which guides the AI through implementation, validation, and iteration. This structured context-driven workflow aims to reduce AI failures by addressing "context failures" rather than model limitations.

Quick Start & Requirements

1. Clone the repository: git clone https://github.com/coleam00/Context-Engineering-Intro.git && cd Context-Engineering-Intro
2. Requires Claude Code (or a compatible AI coding assistant) for executing the /generate-prp and /execute-prp commands.
3. Setup involves editing CLAUDE.md, INITIAL.md, and populating the examples/ directory.

Highlighted Details

• Positions Context Engineering as a superior alternative to prompt engineering for AI coding assistants.
• Emphasizes the use of code examples for demonstrating patterns in structure, testing, integration, and CLI usage.
• The workflow includes automated generation of detailed implementation blueprints (PRPs) and execution with validation loops.
• Provides specific guidance on structuring feature requests (INITIAL.md) and global rules (CLAUDE.md).

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The repository itself is not explicitly licensed. The README does not specify licensing for the template's content or any included code.

Limitations & Caveats

The template explicitly states it does not focus on RAG and tools, with future additions planned. The effectiveness is heavily dependent on the capabilities of the AI coding assistant used (specifically Claude Code is mentioned) and the quality/comprehensiveness of the provided context files and examples.