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I'd like to share some insights on using AI to translate articles. Here is the core philosophy:

The best translation is actually rewriting.
Achieving high-quality results requires breaking the process down into steps.

However, this depends on the scenario. For general translation needs, a single rewrite is usually sufficient. Given the capabilities of current Large Language Models—especially models like Gemini 3 Pro—the quality of a one-shot rewrite is already quite impressive.

If you are aiming for professional-grade translation, it is necessary to have the AI proofread and polish the text after that initial rewrite.

Crucially, do not attempt to cram translation, proofreading, and polishing into a single prompt, unless the content is extremely short.

The main reason is something I mentioned yesterday: while models can handle very long inputs, they tend to cut corners and suffer from severe hallucinations if the required output is too long.

Imagine translating a 2,000-word article. If you ask the model to rewrite, then proofread, and finally polish it all at once, the output could easily balloon to 5,000 - 6,000 words. By the time it gets to the end, the quality will degrade significantly.

Therefore, translation, proofreading, and polishing are best performed independently.

Let's start with translation. The prompt doesn't need to be overly complex. Simply asking it to "rewrite" works well, provided you specify the following:
- The style of the article
- A glossary of common terms or mappings
- The target audience (optional)

For proofreading, you need to provide both the original text and the translation. The goal here is to check for omissions and errors. If your requirement for precision isn't extremely high, you can skip this step.

For polishing, you do not need the original English text anymore; the translation alone is sufficient. At this stage, you just need the model to check if the results flow smoothly and if the phrasing adheres to native expression habits. The original text is no longer necessary.

Additionally, if the article is too long, you should split it into blocks. It is best to split by natural chapters or paragraphs; usually, splitting at the paragraph level is enough.

As for how to ensure continuity between the current block and the previous one, a simple and effective method is to add the source text and translation of the previous block into the context. This allows the translation of the next block to reference the content and style of the one before it.

Regarding how much history to retain, that depends on the model and the size of the blocks. Usually, the source and translation of the immediately preceding block are enough; you don't need too much history. In fact, sometimes providing no history at all works fine.

This process can be automated via a program using an API, or done manually within the model interface. Personally, I use Gemini. I create different "Gems" for different prompts, and when I need them, I simply paste the content into the corresponding Gem.

I'd like to share some insights on using AI to translate articles. Here is the core philosophy: The best translation is actually rewriting. Achieving high-quality results requires breaking the process down into steps. However, this depends on the scenario. For general translation needs, a single rewrite is usually sufficient. Given the capabilities of current Large Language Models—especially models like Gemini 3 Pro—the quality of a one-shot rewrite is already quite impressive. If you are aiming for professional-grade translation, it is necessary to have the AI proofread and polish the text after that initial rewrite. Crucially, do not attempt to cram translation, proofreading, and polishing into a single prompt, unless the content is extremely short. The main reason is something I mentioned yesterday: while models can handle very long inputs, they tend to cut corners and suffer from severe hallucinations if the required output is too long. Imagine translating a 2,000-word article. If you ask the model to rewrite, then proofread, and finally polish it all at once, the output could easily balloon to 5,000 - 6,000 words. By the time it gets to the end, the quality will degrade significantly. Therefore, translation, proofreading, and polishing are best performed independently. Let's start with translation. The prompt doesn't need to be overly complex. Simply asking it to "rewrite" works well, provided you specify the following: - The style of the article - A glossary of common terms or mappings - The target audience (optional) For proofreading, you need to provide both the original text and the translation. The goal here is to check for omissions and errors. If your requirement for precision isn't extremely high, you can skip this step. For polishing, you do not need the original English text anymore; the translation alone is sufficient. At this stage, you just need the model to check if the results flow smoothly and if the phrasing adheres to native expression habits. The original text is no longer necessary. Additionally, if the article is too long, you should split it into blocks. It is best to split by natural chapters or paragraphs; usually, splitting at the paragraph level is enough. As for how to ensure continuity between the current block and the previous one, a simple and effective method is to add the source text and translation of the previous block into the context. This allows the translation of the next block to reference the content and style of the one before it. Regarding how much history to retain, that depends on the model and the size of the blocks. Usually, the source and translation of the immediately preceding block are enough; you don't need too much history. In fact, sometimes providing no history at all works fine. This process can be automated via a program using an API, or done manually within the model interface. Personally, I use Gemini. I create different "Gems" for different prompts, and when I need them, I simply paste the content into the corresponding Gem.

Prompt for High-Quality Translation ---- Prompt ---- Please rewrite the following article into accessible, fluent, and engaging {{Simplified Chinese}}. Core Requirements: - Audience & Style: Target general readers interested in AI. Adopt a storytelling style that is clear and easy to understand, rather than the tone of an academic paper. - Accuracy First: Core facts, data, and logic must remain strictly consistent with the original text. - Flow & Fluency: Prioritize authentic target language sentence structure. Break down long, complex sentences into more natural, shorter phrases. - Standard Terminology: Use industry-recognized standard translations for technical terms (e.g., {{overfitting -> 过拟合}}). Upon the first occurrence of a term, include the original text in parentheses. - Preserve Formatting: Maintain the original Markdown formatting, including headings, bolding, italics, and images. - Contextual Annotation: If technical terms are difficult for a layperson to understand, or if missing background knowledge hinders comprehension, add appropriate annotations to facilitate understanding. Wrap these annotations in bold parentheses like this: (annotation). Glossary / Common Vocabulary: {{- AI Agent -> AI 智能体 - LLM -> 大语言模型}} Now, please rewrite the content below:

avatar for 宝玉
宝玉
Wed Dec 03 03:16:09
More such AI tools and projects in https://t.co/BvTc8nQQW5:

Get access to 100+ AI Agent, RAG, LLM, and MCP tutorials with open-source code - All for FREE.

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avatar for Unwind AI
Unwind AI
Wed Dec 03 03:15:33
Works with OpenAI, Anthropic, LangChain or any framework.

Python and TypeScript SDKs available.

Check out the repo and ⭐️ it: https://t.co/0WuTfoISXC

Works with OpenAI, Anthropic, LangChain or any framework. Python and TypeScript SDKs available. Check out the repo and ⭐️ it: https://t.co/0WuTfoISXC

More such AI tools and projects in https://t.co/BvTc8nQQW5: Get access to 100+ AI Agent, RAG, LLM, and MCP tutorials with open-source code - All for FREE.

avatar for Unwind AI
Unwind AI
Wed Dec 03 03:15:30
Open-source context layer for AI agents to self-learn and improve.

It goes beyond memory to store conversations, observe tasks, learn from past executions, and collect SOPs into agent's long-term memory.

Install and run in literally 2 lines of code.

100% open-source.

Open-source context layer for AI agents to self-learn and improve. It goes beyond memory to store conversations, observe tasks, learn from past executions, and collect SOPs into agent's long-term memory. Install and run in literally 2 lines of code. 100% open-source.

The problem: AI agents work perfectly once, then fail mysteriously the next time. They don't remember what worked before or why they failed. Scattered memory stores, RAG pipelines and logs making it impossible to analyze. @acontext_io solved this with a unified context platform.

avatar for Unwind AI
Unwind AI
Wed Dec 03 03:15:03
RT @indie_maker_fox: 🎉 分享一个基于shadcn/ui的开源组件库 blocks

我大概看了一遍,里面有蛮多非常实用的组件,尤其是跟table有关的,效果不错,推荐下。来自开发者 @EphraimDuncan_ https://t.co/eRuamo8K…

RT @indie_maker_fox: 🎉 分享一个基于shadcn/ui的开源组件库 blocks 我大概看了一遍,里面有蛮多非常实用的组件,尤其是跟table有关的,效果不错,推荐下。来自开发者 @EphraimDuncan_ https://t.co/eRuamo8K…

🔥 The best AI SaaS boilerplate - https://t.co/VyNtTs0jSX 🚀 The best directory boilerplate with AI - https://t.co/wEvJ1Dd8aR 🎉 https://t.co/bh1RxeERuY & https://t.co/zubXJCoY92 & https://t.co/tfQf8T7gGF

avatar for Fox@MkSaaS.com
Fox@MkSaaS.com
Wed Dec 03 03:10:15
Still pinch myself I get to work with DG and see the legend in action. A must listen for those who loves investing.

Still pinch myself I get to work with DG and see the legend in action. A must listen for those who loves investing.

GP @a16z AI x Infra. 💙 Data, AI and dev tools. Portcos: @elevenlabsio @FAL @Ideogram_ai @mintlify @motherduck @usepylon @resend @reductoai @StainlessAPI

avatar for Jennifer Li
Jennifer Li
Wed Dec 03 03:07:44
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