# Context Engineering: Engineering Information for AI Systems

- **Author**: J.C. Ködel
- **Format**: PDF
- **Length**: 371 pages
- **Source File**: /library/Context Engineering/source-file.pdf
- **Top-Level Sections**: 36
- **Note**: Sections 1–2 are orientation/front matter; the main technical argument begins in Section 3.

## Contents

1. About the author
2. Map of the trilogy
3. How LLMs use context
4. Tokens and context windows
5. Memory and limits
6. The context cycle
7. Context rot: why large contexts degrade quality
8. Token economics: the real cost of bad context
9. Parametric calculation: cost of irrelevant context
10. Prompt engineering vs context engineering: why the prompt became a second-order variable
11. Specifications
12. Living documentation
13. ADRs
14. Conventions
15. Persistent context files
16. Project organization
17. Modularization
18. Context for brownfield projects
19. Context layers
20. Context packing
21. Context recovery
22. Context validation
23. Context compression
24. Context isolation
25. RAG vs direct context
26. MCP and tools as dynamic context
27. Context security and trust
28. Where to start
29. Development loops with AI
30. Measuring context: how to evaluate whether your context improves results
31. Principles applied: chat, IDE, terminal and CI
32. Teams: context as a repository asset
33. Preparing a project (from scratch and from a legacy system)
34. A complete AI-guided implementation
35. Post-mortem: where the context failed and how it was recovered
36. References