Come far sì che le traduzioni AI sembrino native

Why AI translations sound robotic

Generic AI translation models are trained on broad multilingual corpora. They learn grammar, idiom, and register from millions of documents, but they have no knowledge of your brand, your product terminology, or the specific tone your customers expect. The result is output that is linguistically correct but culturally or tonally wrong for your brand.

The most common symptoms are inconsistent terminology across translated pages, overly formal or overly casual register depending on the model's defaults, literal translations of brand-specific phrases that should be adapted rather than translated, and missing cultural resonance in markets where a direct translation loses the implied meaning of the original.

None of these problems require a better model. They require giving the model better inputs before translation begins.

What gives AI translations a native feel?

Glossaries: teach the model your terminology

A translation glossary is a defined list of terms with their approved translations or handling instructions. When applied before translation, a glossary tells the model how to handle product names, brand terms, and technical vocabulary rather than leaving those decisions to the model's default behavior. Glossary enforcement at translation time, rather than as a post-processing correction, produces first-pass output that already uses your approved terminology throughout.

Style guides: define tone and register

A style guide specifies how your brand communicates: formal or conversational, active or passive voice, how numbers and dates are formatted, and how your brand addresses customers in each market. When applied via retrieval-augmented generation, a style guide gives the translation model explicit register and tone instructions rather than defaulting to the model's average training voice.

Translation memory: learn from approved content

Translation memory stores every approved translation and makes it available for reuse in future jobs. Beyond cost savings, TM shapes the AI's output by providing examples of how your brand has already translated similar phrases. Smartling's AI Adaptive Translation Memory goes further: it automatically adapts TM matches with scores between 50 and 99.9 percent to fit new content grammatically and contextually, rather than requiring exact matches for reuse. 

Retrieval-augmented generation: brand context at translation time

Retrieval-augmented generation (RAG) is the mechanism by which glossaries and style guides are applied at translation time rather than as a post-processing filter. Rather than translating first and correcting afterward, RAG retrieves relevant brand context and provides it to the model as part of the translation instruction, producing output that reflects your brand from the first string.

How does Smartling make AI translations feel native?

Smartling's AIHT applies brand context before the first string is translated. Glossaries, style guides, and translation memory are retrieved via RAG and provided to the model as part of each translation instruction. AI Adaptive Translation Memory automatically adapts existing TM matches to fit new content rather than requiring exact matches for reuse, compounding brand consistency across every job.

The result is that AIHT achieves MQM scores of 98 or above, exceeding the 95 to 97 industry benchmark for traditional human translation from most language service providers, at half the cost and twice the speed. The MQM score reflects both technical accuracy and brand consistency, not only error rates.

Help doc: Introduction to the Style Guide

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