Context Engineering

A practical guide to designing and optimizing context for AI applications. This repository contains code examples and patterns that demonstrate how to think about context engineering across different use cases and programming languages.

🎯 What is Context Engineering?

Context engineering is the practice of thoughtfully designing the information (context) you provide to AI models to achieve better, more reliable results. It's about understanding:

• What information matters for your AI task
• How to structure that information effectively
• When and how much context to include
• How to validate and improve your context over time

Great context engineering is the difference between an AI system that feels like magic and one that feels like a reliable tool. It's about being intentional with every piece of information you pass to your model.

📚 Repository Structure

This repository is organized by use case, with implementations in both Python and JavaScript where applicable:

context-engineering/
├── use-cases/
│   ├── retrieval-augmented-generation/
│   │   ├── python/
│   │   └── javascript/
│   ├── multi-step-reasoning/
│   │   ├── python/
│   │   └── javascript/
│   ├── code-generation/
│   │   ├── python/
│   │   └── javascript/
│   ├── information-extraction/
│   │   ├── python/
│   │   └── javascript/
│   └── [more use cases]
├── patterns/
│   ├── context-selection.md
│   ├── context-formatting.md
│   ├── context-validation.md
│   └── context-optimization.md
└── README.md

🔍 Key Concepts

1. Context Selection

Which information should actually be included? Learn to identify signal from noise.

2. Context Formatting

How should you structure and present context? Organization matters for model understanding.

3. Context Validation

How do you know your context is working? Measuring and iterating on context quality.

4. Context Optimization

How do you scale context efficiently while maintaining quality?

🚀 Getting Started

Each use case folder contains:

• Example implementations in Python and/or JavaScript
• README explaining the specific context engineering approach
• Test cases demonstrating expected behavior
• Configuration showing different context strategies

Running Examples

Refer to the specific use case directory for setup instructions. Each will have its own requirements and dependencies.

📖 Use Cases

Retrieval-Augmented Generation (RAG)

Learn how to select and structure retrieved documents to improve factual accuracy and relevance.

Multi-Step Reasoning

Understand how to provide context that guides models through complex multi-step problems.

Code Generation

Explore context patterns that help models generate accurate, contextually-aware code.

Information Extraction

See how structured context improves extraction accuracy from unstructured data.

💡 Best Practices

1. Start Simple: Begin with minimal context and add incrementally
2. Measure Impact: Always quantify how context changes affect your results
3. Iterate Deliberately: Change one thing at a time to understand what works
4. Document Context: Explain why each piece of context is included
5. Test Edge Cases: Ensure your context handles unusual or adversarial inputs

🤝 Contributing

This repository is a collection of patterns and examples. Feel free to add new use cases, improve existing examples, or share better context engineering approaches.

📝 License

See LICENSE file for details.

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Remember: Great context engineering is about being intentional. Every token matters. Ask yourself: "Does this context make the model's job easier or harder?"