Masanori Asano | Codex Translation

Codex Translation A translation workflow where translators store translation patterns in a structured database and use them with AI systems such as RAG.

@booleanbeyondIN Thanks — that’s exactly the idea. I want AI to work from verified translation patterns, not from stylistic guesswork.

@codex_trans Love the idea—patterns in a structured DB plus RAG feels like giving the AI a Swiss Army knife of verified phrasing before it invents feelings for the sunrise again.

Codex Translation A translation workflow where translators store translation patterns in a structured database and use them with AI systems such as RAG.

If you’re still translating everything from scratch, you’re already falling behind. The shift is simple: From “translator” to “system builder” I wrote about it here ↓ codextranslation.com/2026/03/19/why…

90% of translators will struggle in the AI era. Not because they lack skill. But because they don’t have a system. The future belongs to those who build leverage. Here’s what that means ↓ codextranslation.com/2026/03/19/why…

Most translators are focusing on the wrong thing. It’s not about language anymore. It’s about systems. AI is already changing the game. Here’s how to stay ahead ↓ codextranslation.com/2026/03/14/why…

Translation isn’t about words anymore. It’s about patterns. If you don’t see the structure, AI will replace you.

Translation is not language. It’s structure.

Patent translator × AI. Building a knowledge-driven translation system (Codex) and training AI to improve accuracy and speed. Translation is shifting from human skill to knowledge systems.

Codex Translation Method AI translation method based on structured knowledge databases. Components: 1 Phrase database 2 Terminology database 3 Technical knowledge database 4 AI translation 5 RAG retrieval

I’m experimenting with an AI translation workflow: Knowledge base (Codex) ↓ RAG retrieval ↓ LLM translation ↓ Human editing For domains like patent translation with repetitive structures, this pipeline seems surprisingly effective.

@renatobeninatto @perplexity_ai I’m experimenting with a translation pipeline using RAG + a knowledge base + LLM translation. It seems especially effective for patent translation due to the repetitive sentence structures.

I'm on the waitlist for @perplexity_ai's new agentic browser, Comet: perplexity.ai/comet

@GermanENTrans I’m experimenting with a translation pipeline using RAG + knowledge database + LLM translation. It seems particularly powerful for patent translation because of the repetitive structures.

I'm hosting "Independent tools for human writing, translation and editing". Join us on October 24 to see why local control still matters in the Online Age of AI and #Trashlation. linkedin.com/events/indepen…

If translators combine: • knowledge databases • RAG retrieval • LLM translation Productivity could increase dramatically. The role of translators may shift from translator → AI translation architect.

Patent translation is surprisingly compatible with AI. Why? • repetitive sentence structures • standardized expressions • technical terminology patterns With a structured knowledge base, translation may become mostly editing.

In the past, a translator’s asset was skill. In the AI era, it may be: Skill + Knowledge Database + AI. Translators who build their own knowledge base will have a huge advantage.

I’ve been experimenting with an AI translation workflow: Codex (knowledge DB) ↓ LangChain retrieval ↓ LLM translation ↓ Human editing This pipeline can drastically increase translator productivity.

In the AI era, translators may need something like a Codex. A structured knowledge base of: • technical phrases • patent patterns • translation structures The more you accumulate, the faster AI-assisted translation becomes.

The future of translation may look like this: Source text → RAG retrieval (Codex / knowledge base) → LLM translation → Human editing Translation will shift from writing to editing AI outputs. Knowledge databases will become the core asset of translators.

In the AI era, a translator’s asset may shift from translation memory to knowledge databases. TM → Codex. The future of translation may belong to translators who build structured knowledge bases.

Translation is changing fast. In the AI era, a translator's most valuable asset may no longer be CAT tools. It may be knowledge databases. Codex may become the new translation memory.

In the AI era, a translator’s biggest asset is not experience. It’s data. A translator with 20 years of experience can lose to someone who has built a large translation knowledge database. Translation is becoming a data-driven profession.