context-engineering

Welcome to the Context Engineering for AI course, designed to equip learners with essential principles, techniques, and practices for creating robust, reliable, and efficient AI systems. This comprehensive guide covers everything from developing precise prompts to enabling autonomous systems capable of operating independently.

Course Overview
The 2026 edition of this course stands out by addressing the significant reorganization in the field that took place between 2023 and 2026. It introduces four integral disciplines: prompt, context, harness, and loop, culminating with a cohesive architecture for agentic systems. For a detailed record of updates, please refer to the CHANGELOG.md.

Course Structure

The curriculum comprises nine well-structured modules:

1. Foundations of Context Engineering

   • Learn the criticality of context and its evolution.
   • Understand core principles of effective context design.

2. Advanced Prompting Techniques

   • Dive into the anatomy of advanced prompts and foundational prompting techniques.
   • Explore strategic prompting for enhanced results.

3. Retrieval-Augmented Generation (RAG)

   • Get familiar with the principles of RAG and optimally managing knowledge bases.
   • Study the generation and synthesis processes involved in this paradigm.

4. Optimizing the Context Window

   • Master the context window operations and strategies for relevance enhancement.
   • Learn about contextual compression and long-horizon agent engineering.

5. From RAG to Agents

   • Understand the foundation of AI agents, including designing and integrating tools.
   • Develop frameworks and architectures that define agent capabilities.

6. Evaluation, Testing, and Security

   • Evaluate context quality alongside agent performance.
   • Explore testing methodologies and security requirements for context-aware systems.

7. The Frontier

   • Investigate the evolving landscape of multi-modal and computer-using agents.
   • Discuss the business and ethical implications of agentic AI.

8. Agentic Engineering

   • Learn about the harness mechanisms that determine agentic success or failure.
   • Understand the loop engineering processes and structuring effective AI teams.

9. Graph Engineering and Autonomous Meta-Harness Systems

   • Study graph engineering principles and the orchestration of tasks through execution graphs.
   • Configure autonomous systems that can adjust their own architectures.

Practical Features

The course includes a dependency-free reference harness located in the code/ directory, featuring:

cd code
python3 -m unittest discover -s tests -t .    # 134 tests
python3 examples/03_agent_loop.py             # the same model under two harnesses
python3 examples/09_meta_harness.py           # a reward hack caught by the gate

Additionally, there are ten auto-graded exercises that facilitate hands-on learning:

python3 exercises/check.py                    # grade all ten
python3 exercises/check.py ex04               # grade one

For advanced users, six optional live-model labs are included, showcasing the realities of non-determinism and other complexities encountered in a live environment. These labs require an API key but are low-cost and exit cleanly without one.

Course Resources

Additional resources within the repository include:

• CHEATSHEET.md: A quick reference for decisions throughout the course.
• ANTI_PATTERNS.md: A diagnostic guide organized by symptom to identify and resolve common issues.
• INDEX.md: A comprehensive index connecting concepts, lessons, and implementations.
• FINAL_PROJECT.md: A practical project to design, measure, and navigate an entire agentic system.

Learning Pathways

The course is structured to take approximately 35–44 hours to complete, and learners are encouraged to engage in hands-on tasks to gain practical insights before reviewing solutions. Three suggested pathways are available for navigating through the content, whether building a new system, debugging existing issues, or pursuing comprehensive field knowledge.

This course presents an opportunity for learners to delve deep into the world of Context Engineering, enhancing the ability to build functional and reliable AI systems for various applications.