关于 AI 翻译工作流的一些工程化思考

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最近看到宝玉老师在聊翻译的的迭代，很有意思。‘老朋友’总是能带来一些新的视角，这种开源分享的精神确实让人佩服。

这也让我想起自己这两年的折腾。从去年上半年开始，为了解决大量海外文档的阅读效率问题，被迫从一个翻译小白开始捣鼓这套东西。当时所幸赶上了 4o-mini 这种高性价比模型的发布，让我有机会把这件事做得更细致一些。

最开始，我的诉求很简单：把英文变成中文。但很快我发现，翻译这事儿，光有信雅达这种抽象概念是不够的。在产品视角下，它其实是一组具体的指标：Accuracy（准确性）、Clarity（清晰度）、Naturalness（自然度）和 Acceptability（可接受度）。这大概就是所谓的 ACNA 翻译美学。甚至一度想引入 BLEU 或 COMET 这种机器翻译的量化指标来做评估。老程序员的职业病，总想把感性的东西数据化。

在探索过程中，也踩过不少坑。

比如宝玉老师之前提到的 “Rewrite”（重写） 策略。这确实是一个神奇的词，在很长一段时间里，它是我提升翻译流畅度的银弹。但随着场景变复杂，我发现重写存在一个致命的 Bug：聪明的模型为了让句子好读，有时会擅自篡改原意，甚至产生幻觉。对于严谨的法务类文档或说明书来说，这种自作聪明是灾难性的。

于是开始思考：如何在一个标准化的流水线里，既保证准确性（Accuracy），又能兼顾优雅（Elegance）？

这本质上是一个控制的问题。我们需要确定目标读者是谁、市场定位是什么，再反推翻译策略。经过不断的迭代，现在沉淀下来一套比较稳定的流水线，大概分四步：

第一步：定义翻译指南

这其实是最关键，但也最容易被忽视的一步。 在动工前，先让 AI 生成一份翻译指南。这就像写 PRD 一样，我们需要明确：这篇文章是给谁看的？是严肃的学术探讨，还是轻松的博客？核心受众的年龄层和文化背景是什么？ 有了这份指南，后续所有的翻译动作就有了参考。

第二步：合理的切分

很多翻译工具的问题在于切得太碎。机械地按字数切分，AI 就容易只见树木不见森林。 我的做法是按自然段落切分，并且在处理每一段时，强制带上上下文。语境是翻译的灵魂，没有上下文的翻译，就像没有前因后果的对话，注定是生硬的。

第三步：双重加工

这部分我做了一些改良，不再迷信 “Rewrite”，而是回归到 “Translate + Refine” 的逻辑：

初稿：老老实实地翻译，指令核心是优先原意。这里不求文采，只求不错。
优化：这一步是提质的关键。我把宝玉提到的校对和润色合并了。
去翻译腔：让句子读起来像母语。
风格对齐：这里就用到了第一步的指南。如果原文是诗歌，优化指令就是富有诗意；如果原文是说明书，指令就是严谨直白。
拒绝模糊指令：不要只告诉模型要信雅达，它听不懂。你要告诉它根据指南中的定义，调整语气和用词。
第四步：交付

眼见着这套流程跑通后，翻译质量确实有了肉眼可见的提升。以前啃不动的 Twitter 长推文、Youtube 的视频，现在读起来都很顺畅。

当然也有个最大的 Bug，就是 Token 消耗量有点大。但这在长期来看，应该不是问题。

最后聊两句对未来的想法。

在这个 Case 里，我明显感觉到，虽然工程技巧（Prompt Engineering）依然很重要，能解决很多 Edge Case，但模型基础能力的提升才是那个普惠级的变量。

现在的我们，还在费尽心思地搭建流水线、写复杂的提示词，本质上是在用工程手段弥补模型的不足。

未来的翻译想来可能不再是一个需要被单独拎出来的动作。未来的 AI 产品，或许能做到无感知的语言消融，我们看到的任何信息，都已经自动、完美地转化成了你最舒适的母语形态。那时候，我们今天讨论的这些复杂的 Pipeline，可能都会被封装进一个简单的 API，甚至就直接内化在模型的基础能力里。

一个人人都能无障碍获取全球信息的时代，已然到来。

Engineering Epiphanies: Building an AI Translation Workflow

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I recently caught Baoyu’s thoughts on iterative translation, and honestly, it was fascinating. There’s something about old friends bringing fresh perspectives—that spirit of open-source sharing never fails to inspire.

It immediately reminded me of my own journey down the rabbit hole over the last two years. Starting early last year, fueled by the desperate need to consume massive amounts of overseas content efficiently, I was forced to evolve from a “translation noob” into someone who actually builds these tools. Luckily, the release of high-performance, cost-effective models like 4o-mini gave me the breathing room to refine this process into something serious.

At first, my goal was dead simple: turn English into Chinese. But I quickly realized that abstract, classical ideals like “Faithfulness, Expressiveness, and Elegance” (the holy trinity of Chinese translation theory) aren’t enough when you’re building a product. Product thinking demands strict metrics: Accuracy, Clarity, Naturalness, and Acceptability. Let’s call it the ACNA aesthetic. I even briefly considered obsessing over quantitative metrics like BLEU or COMET. Call it the veteran programmer’s curse: the urge to turn every subjective feeling into hard data.

The road was paved with pitfalls.

Take the “Rewrite” strategy Baoyu mentioned. For a long time, “Rewrite” was my silver bullet for fluency. But as my use cases grew complex, I found a fatal bug: in their eagerness to please and make sentences “smooth,” smart models would silently alter the meaning or, worse, hallucinate entirely. For precise legal docs or technical manuals, that kind of “smart” is catastrophic.

So, the question became: How do we build a standardized pipeline that ensures Accuracy without sacrificing Elegance?

Fundamentally, this is a control problem. You have to define the target audience and market positioning, then reverse-engineer the translation strategy. After endless iterations, I’ve settled on a stable, four-step pipeline:

Step 1: The “North Star” Guide

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This is the most critical yet most overlooked step. Before translating a single word, I ask the AI to generate a specific Translation Guide. Think of it like writing a PRD (Product Requirement Document). Who is reading this? Is it a rigorous academic paper or a breezy blog post? What is the cultural background of the audience? With this guide, every subsequent action has a frame of reference.

Step 2: Sensible Slicing

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Most tools fail because they chop text too finely. When you mechanically slice by character count, the AI misses the forest for the trees. My approach is to slice by natural paragraphs and force the model to carry the context of the previous segment. Context is the soul of translation; without it, text feels like a conversation with no memory—stiff and disjointed.

Step 3: The Dual-Process Method

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I’ve moved away from the blind “Rewrite” method and returned to a “Translate + Refine” logic:

• Drafting: Translate honestly. The core instruction here is fidelity to the intended meaning. Forget style; just don’t get it wrong.
• Polishing: This is where the magic happens. I combine proofreading and refining into one pass.
  • Kill the “Translationese”: Make it sound native.
  • Style Alignment: Refer back to Step 1. If it’s poetry, ask for rhythm; if it’s a manual, demand precision.
  • No Vague Prompts: Don’t just tell the model to be “elegant”—it doesn’t know what that means. Tell it to “adjust tone and vocabulary according to the Guide.”

Step 4: Delivery

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Once this loop was closed, the quality leap was visible to the naked eye. Long Twitter threads and YouTube transcripts that used to be a chore to read are now effortless.

Sure, the biggest bug remains: Token consumption is high. But in the long run, that’s a solvable constraint.

A Final Thought on the Future

In this specific case, I realized that while Prompt Engineering is still vital for handling edge cases, the real variable that lifts all boats is the foundational capability of the model.

Right now, we are still painstakingly assembling pipelines and crafting complex prompts—essentially using engineering to patch over current model limitations.

But I suspect translation won’t be a standalone “action” for much longer. Future AI products will likely achieve a kind of imperceptible language dissolution. Every piece of information you encounter will be automatically, flawlessly rendered into your native tongue’s most comfortable form.

In that world, the complex pipelines we discuss today will be encapsulated into a simple API call, or dissolved entirely into the model’s intuition. A conceptual era where global information flows without friction is likely closer than we think.