今年除了 AI，还是 AI。。。
对我来说，今年不是用 AI 提高了生产力，而是提高了我的词汇量，每天打开技术圈：
Prompt 牛X..,
Agent 改变世界...
Context Engineering 很重要...
Loop Engineering 才是未来...
感觉没学几天，之前的知识都 Dead 了，已经像错过各种新的世界～
从让 AI 回答问题 → 给 AI 提供信息 → 让 AI 自己干活 → 最后持续自动运行。
所以现在每天就是争取先把这些新单词背下来，从年初的 LLM、 MCP、Agent Skills、Claude Code、OpenCode、Remotion，到现在开始背 Agentic Coding、RAG、Context Window、ReAct……
接下来我们就把 AI 领域最常见的术语梳理一遍：
一、编程范式（Coding Paradigm）
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| Vibe Coding |
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| Context Engineering |
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| Agentic Coding |
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| AI Native Development |
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二、工程角色（Engineer Role）
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| Harness Engineer |
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| Loop Engineer |
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| Context Engineer |
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| AI Product Engineer |
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| Agent Operator |
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三、模型与底层（Model & Fundamentals）
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| LLM
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| GPT |
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| Token |
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| Context Window |
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| Inference |
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| Hallucination |
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| Temperature |
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| Top-p / Top-k |
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| Embedding |
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| Fine-tuning |
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| RLHF |
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| MoE
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| Multimodal |
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| Transformer |
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| KV Cache |
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| Latency |
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| Throughput |
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四、Prompt 工程（Prompt Engineering）
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| Prompt |
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| System Prompt |
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| Prompt Engineering |
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| Zero-shot |
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| Few-shot |
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| Chain of Thought（CoT） |
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| ReAct |
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| Role Prompting |
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| Structured Output |
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| Prompt Chaining |
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| Self-Consistency |
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| Tree of Thoughts（ToT） |
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五、Agent 架构（Agent Architecture）
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| Agent |
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| Multi-Agent |
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| Subagent |
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| Tool Use / Function Calling |
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| Planning |
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| Reflection |
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| Memory |
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| Agent Loop |
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| Environment |
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| Observation |
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| Execution |
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| Long-term Memory |
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六、Harness 组件（AI Agent 能力模块）
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| Harness |
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| Skills |
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| Context |
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| MCP
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| Permission |
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| RAG
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| Tool Registry |
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| Session |
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| Knowledge Base |
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七、Loop 工具（自动化编排与自主进化）
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| /loop |
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| /goal |
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| Cron |
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| Worktree |
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| Workflow |
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| Scheduler |
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| Checkpoint |
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| Human-in-the-loop |
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八、工具生态（Tool Ecosystem）
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| Claude Code（cc） |
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| Codex CLI |
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| Cursor |
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| Windsurf |
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| GitHub Copilot |
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| Cline / Roo Code |
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| Aider |
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| Devin |
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| Continue |
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| Qoder |
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| Trae |
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| ZCode |
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| OpenHands |
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| Bolt |
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| Lovable |
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九、评估与安全（Evaluation & Safety）
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| Eval |
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| Alignment |
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| Guardrail |
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| Red Teaming |
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| Prompt Injection |
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| Context Poisoning |
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| Sandbox |
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十、AI 工程演进路线（Evolution）
AI 工程能力正在逐步上移：从控制模型输出，到控制上下文，再到控制系统，最终演化为持续自治系统。
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