Context Engineering

Overview

The Context Engineering for AI course offers an in-depth exploration of principles, techniques, and practices vital for crafting robust and reliable AI systems. Designed for the 2026 edition, the course reflects significant advancements in context engineering since 2023, teaching concepts organized into four nested disciplines: prompt → context → harness → loop. The course concludes with a cohesive architecture for agentic systems, ensuring a comprehensive understanding of the field.

Course Structure

Nine Modules

The course comprises nine structured modules, each building upon the last to cultivate a strong foundation and advance understanding:

1. Foundations of Context Engineering
   Discover the essentials of context and its critical role in AI, supplemented by core principles of effective context design.

   • Module 1: Foundations of Context Engineering

2. Advanced Prompting Techniques
   Dive into advanced prompting strategies to optimize AI interactions and outputs.

   • Module 2: Advanced Prompting Techniques

3. Retrieval-Augmented Generation (RAG)
   Learn about RAG and how to build and manage a knowledge base effectively.

   • Module 3: Retrieval-Augmented Generation

4. Optimizing the Context Window
   Master techniques to optimize the context window for enhanced performance of AI systems.

   • Module 4: Optimizing the Context Window

5. From RAG to Agents
   Transition from RAG concepts to designing trustworthy autonomous agents.

   • Module 5: From RAG to Agents

6. Evaluation, Testing, and Security
   Focus on methods for evaluating context quality, agent performance, and implementing security measures.

   • Module 6: Evaluation, Testing, and Security

7. The Frontier
   Explore multi-modal agents and the evolving landscape of agentic AI.

   • Module 7: The Frontier

8. Agentic Engineering
   Understand harness design and loop engineering to optimize agent performance and teamwork.

   • Module 8: Agentic Engineering

9. Graph Engineering and Autonomous Meta-Harness Systems
   Learn about graph engineering and how to govern systems that can adapt and modify themselves.

   • Module 9: Graph Engineering and Autonomous Meta-Harness Systems

Hands-On Experience

For practical application, the course includes runnable code available in a dependency-free reference harness under code/. This allows users to execute tests and exercises seamlessly, such as:

cd code
python3 -m unittest discover -s tests -t .    # Run all tests
python3 examples/03_agent_loop.py             # Execute agent loop example

The course also features ten auto-graded exercises and optional live-model labs to reinforce learning through hands-on tasks and real-world scenarios. Additionally, a validation tool ensures the course’s integrity by running checks on the materials provided:

python3 tools/validate_course.py              # Run validation checks

Supporting Resources

Additional resources are included in the repository to enhance the learning experience:

• CHEATSHEET.md: A concise guide to aid decision-making during the development process.
• ANTI_PATTERNS.md: A diagnostic reference that addresses common issues encountered in AI development.
• FINAL_PROJECT.md: A comprehensive project that encapsulates the knowledge gained throughout the course.

Prerequisites

• Basic proficiency in Python (version 3.10+) is required for completing exercises.
• Familiarity with API clients and concepts related to HTTP requests and JSON is beneficial.
• No machine learning background is necessary; this course focuses on application rather than model training.

Conclusion

The Context Engineering for AI course is designed for anyone wishing to deepen their understanding of AI systems and their architecture. By engaging with the modules, learners will gain critical skills and insights applicable to real-world AI challenges.