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Context Engineering is a comprehensive, open-source project serving as a first-principles handbook for the emerging discipline of context design and optimization in AI. Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. It takes inspiration from thought leaders like Andrej Karpathy and bridges theory with practical examples, offering structured guidance on context orchestration, memory, retrieval, and state control within AI workflows. With extensive materials drawn from research, surveys, and visual explanations, the project acts as both a learning resource and a reference for practitioners looking to improve model behavior by engineering richer inputs.
Features
- First-principles handbook for context design with LLMs
- Focus on context beyond simple prompt engineering
- Covers orchestration, memory, and retrieval strategies
- Research survey and conceptual resources
- Definitions and frameworks for context optimization
- Open-source, community-driven educational repository
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