Llm Action
中文大模型技术专栏：从Transformer到RAG、微调与LangChain实战
作为免费资源，Llm Action 是中文开发者系统性学习大模型工程的优质选择，其结构比零散教程更完整，覆盖微调、RAG、LangChain 与推理优化。但更新不稳定，需自行补充最新模型信息。推荐作为核心学习路径之一，与官方文档搭配使用。
Verified 20h ago · liveness 68/100 · cite: rightaichoice.com/tools/llm-action
- 希望系统性学习大模型技术的AI开发者
- 正在将大模型落地到实际产品的工程师
- 对大模型训练、推理优化感兴趣的ML从业者
- 需要Prompt Engineering和RAG实战指南的研究人员
- 完全零基础的AI初学者（需要一定ML基础）
- 仅需产品级API调用的业务人员
- 需要多语言支持的英文读者
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Skip Llm Action 如果你是完全零基础的 AI 初学者，或只需调用产品级 API 而不打算深入了解模型原理，或英文读者需要多语言支持。
Llm Action 完全免费，适合预算有限的个人学习者和初创团队。相比之下，付费课程如 Coursera 或 Udemy 的大模型专项通常收费 $50-$200，而它零成本提供系统性内容，性价比极高。但需自行投入时间筛选和补充最新资料。
In short
Llm Action — 中文大模型技术专栏：从Transformer到RAG、微调与LangChain实战. Best for 希望系统性学习大模型技术的AI开发者, 正在将大模型落地到实际产品的工程师, 对大模型训练、推理优化感兴趣的ML从业者. Free to use.
What people actually say about Llm Action — is it worth it?
We scanned public community sources for Llm Action on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Llm Action? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 使用免费，中文技术专栏
- 讲解Transformer架构原理
- 预训练与微调（SFT、RLHF）技术解析
- Prompt Engineering策略与案例
- RAG检索增强生成系统搭建教程
- LangChain框架实战
- LlamaIndex框架实战
- 模型量化与蒸馏推理优化方法
- 提供代码示例与可复现实验配置
- GPT、LLaMA、ChatGLM模型评测对比
- 大模型应用落地经验与踩坑记录
- 全中文内容，降低语言门槛
About Llm Action
Llm Action 是一个面向有机器学习基础、正在转向大模型领域的开发者和研究人员的中文技术专栏。它系统讲解大模型的核心原理与工程落地，内容覆盖 Transformer 架构详解、预训练与微调（SFT、RLHF）、Prompt Engineering 策略，以及 RAG 检索增强生成系统的搭建。专栏提供大量代码示例、架构图和参数调优经验，帮助读者从原理到实践完整掌握大模型技术链。对于正在将大模型应用到实际产品的工程师来说，这种工程导向的内容尤其具有直接参考价值。 专栏以全中文撰写，显著降低了中文读者的学习门槛，方便国内 AI 从业者系统性学习。除基础原理外，它还深入 LangChain 与 LlamaIndex 框架实战、模型量化与蒸馏等推理优化方法，并给出 GPT、LLaMA、ChatGLM 等业界模型的评测对比。相比零散的 GitHub 教程，Llm Action 采用结构化路径，从原理到实战循序渐进，配合可复现的实验配置，适合按章节逐步进阶。 不过，专栏对读者的要求并不低，需要具备一定机器学习基础，并非零基础入门材料。同时，更新频率不固定，部分内容可能随模型快速迭代而略显过时，建议搭配最新官方文档和技术博客补充学习。整体而言，Llm Action 是一份扎实的中文技术资料，值得推荐给认真钻研大模型的开发者。
Behind the Verdict
多数大模型教程要么停留在概念层面，要么是零散代码片段，Llm Action 的差异在于它把原理和工程实践串成了一条完整链路。对于已经掌握机器学习基础、正在从传统深度学习转向大模型的开发者，这是一条高效的进阶路径。 我们看重它的几个具体点：Transformer 的底层拆解、SFT 和 RLHF 的对比、RAG 的搭建步骤，以及量化蒸馏这些推理优化方法，覆盖了大模型应用的关键环节。代码示例和实验配置让读者能照着复现，而不是只看理论。 但适用人群有边界。零基础读者会感到吃力，因为专栏默认你懂损失函数、梯度下降和常见的神经网络架构。如果你的目标只是调用现成的 API 做产品原型，那用它就是杀鸡用牛刀，直接读官方文档更快。 和零散的 GitHub 仓库或付费课程比，Llm Action 的优势是免费且结构完整，劣势是更新节奏不定。模型迭代快，部分评测和参数建议可能跟不上最新版本，这点需要读者自己留意。 另一个现实问题是它的发布平台是知乎专栏，阅读体验和代码排版可能不如专业文档站顺畅，检索和跳转也更依赖平台功能。不过对于习惯中文阅读、愿意深入学原理的工程师，这些都不是障碍。
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Real-world workflow fit
Concrete scenarios for the personas Llm Action actually fits — and what changes day-one when you adopt it.
需要搭建一个基于 RAG 的智能问答系统，但对检索和生成如何结合不熟悉。
Outcome: 阅读专栏中 RAG 搭建教程，按照代码示例和参数配置，快速搭建出一个初步可运行的问答系统，并了解优化方向。
想对开源大模型进行微调以适应特定领域，但不知从何开始。
Outcome: 通过专栏中 SFT 与 RLHF 的讲解，掌握微调的基本流程和关键参数，能够启动第一次微调实验。
Use Cases
- 搭建基于RAG的智能问答系统
- 使用Prompt Engineering优化大模型输出
- 对开源大模型进行微调以适应特定领域
- 学习大模型推理加速技术降低部署成本
- 通过LangChain快速构建Agent应用
- 比较不同大模型在中文任务上的表现
Models Under the Hood
as of 2026-09-08
Limitations
专栏内容以中文撰写，更新频率不固定，且深度取决于作者经验。部分文章可能随模型和工具更新而略显过时。无实际可交互产品，仅提供知识分享。
as of 2026-08-24
Verification history
We have re-verified Llm Action 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Llm Action's pricing actually pencils out — and where peers do it cheaper.
Llm Action 完全免费，适合预算有限的个人学习者和初创团队。相比之下，付费课程如 Coursera 或 Udemy 的大模型专项通常收费 $50-$200，而它零成本提供系统性内容，性价比极高。但需自行投入时间筛选和补充最新资料。
Setup time & first value
How long it actually takes to get something useful out of Llm Action — broken out by persona, not the marketing-page minute.
无实际产品，立即访问专栏即可开始阅读。根据个人基础，预计花费数天到数周完成系统学习。
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Llm Action”, and we withheld 6: 6 did not mention Llm Action. We are showing none, because we could not prove any of them are about Llm Action.
Official links
Tools that pair well with Llm Action
Common stack mates teams adopt alongside Llm Action, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Llm Action vs Surge Ai
Do not choose between Llm Action and Surge AI — they serve completely different needs. Llm Action is a free Chinese technical blog for developers learning LLM engineering. Surge AI is a premium expert feedback platform for frontier AI labs aligning models. If you are an engineer building LLM projects, go with Llm Action. If you need expert human data for RLHF or rigorous benchmarks like Antidote, Surge AI is the choice.
Llm Action vs Praktika
These tools are incomparable—Praktika is a language learning app for conversational practice, while Llm Action is a Chinese-language technical blog on LLM engineering. Choose based on your need: improve spoken fluency with AI tutors or deepen your understanding of LLM training and deployment.
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