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English(EN) AI & LLM Terminology Glossary: From Tokens to Orchestration

AI 与 LLM 术语表解释核心工程术语

本文档是面向后端工程师的 AI 和 LLM 工程术语表。它定义了核心概念,如 tokens、context windows、inference 和 parameters,以及与 attention mechanisms 相关的专业术语,如 LoRA、quantization 和 KV cache。该术语表从实践层面解释这些术语,区分它们与研究或营销定义,以帮助日常工作。 AI

影响 为实践者提供了对关键 AI 和 LLM 工程术语的基础理解。

排序理由 这是一篇解释 AI/LLM 术语的文章,而非发布或重要的行业事件。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI 与 LLM 术语表解释核心工程术语

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
这是一篇解释 AI/LLM 术语的文章,而非发布或重要的行业事件。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
79 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · mihir mohapatra ·

    人工智能与大语言模型术语表:从Token到编排

    <p>Every field accumulates jargon, and AI/LLM engineering has accumulated it faster than most — partly because the field is young, and partly because terms get reused across research papers, vendor marketing, and production engineering with slightly different meanings each time.<…