One-sentence definitions for all terms used in the book. English originals are retained alongside Chinese equivalents. Claims are grounded in official documentation; fast-changing items are annotated as of 2026-05.
Corresponding units
- Foundations overview: 01-1 Why use AI tools “correctly”
- LLM and agent mechanics: 01-2 How LLMs and Agents work
- Context engineering detail: 01-4 Context Engineering
- Configuration layer model: 02-1 The Configuration Layer Model
- Privacy and security terms: Appendix B Privacy and Security Checklist and 03-3 Security, Privacy, and Supply Chain Risk
- Cross-tool configuration terms: 02-6 Other Tools Comparison
- Customization terms: 04-1 CLAUDE.md through 04-11 Agent Teams and Sub Agents
- Case study terms: 05-1 OpenClaw through 05-4 Three Approaches Compared
- [1] Chroma, “Context Rot in Long-Context Language Models,” 2025. [Online]. Available: https://research.trychroma.com (verified 2026-06; includes empirical testing across 18 models)
- [2] Liu, et al., “Lost in the Middle: How Language Models Use Long Contexts,” 2023. [Online]. Available: https://arxiv.org/abs/2307.03172
- [3] Anthropic, “Effective context engineering for AI agents,” 2026. [Online]. Available: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
- [4] Yang, et al., “Large Language Models as Optimizers,” 2023. [Online]. Available: https://arxiv.org/abs/2309.03409
- [5] Khattab, et al., “DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines,” 2023. [Online]. Available: https://arxiv.org/abs/2310.03714
- [6] Snyk, “ToxicSkills: 2026 Report on Malicious Skills in the Wild,” 2026. [Online]. Available: https://snyk.io