Implementing Localization using GenAI for Worldwide Expansion

How did BeautifulCode do it?

BeautifulCode began by investigating standard translation solutions like Google Translate. We quickly identified a critical flaw: these tools translated words and phrases in isolation, failing to consider the surrounding user interface and the specific context of the webpage. This resulted in awkward, inaccurate, and often nonsensical text that would damage the user experience.

Our solution was to build a bespoke, context-aware translation pipeline powered by a Large Language Model (LLM).

Engineering a Context-Rich Data Structure

We first designed a unique input data structure. Instead of just sending a word or phrase for translation, this structure included the text itself, its specific location on the webpage, and a description of the surrounding context. This ensured the AI had the full picture before attempting a translation.

Crafting a Sophisticated Prompt

We developed a detailed prompt that instructed the LLM on the client's specific business tone and brand voice. The prompt was engineered to use the rich contextual data from our input structure, guiding the model to produce translations that were not only accurate but also culturally appropriate and consistent with the brand's identity.

Leveraging Llama 3 for High-Quality Translation

We chose Llama 3 as the core translation engine for its advanced reasoning and language generation capabilities. By feeding it our context-rich data and specialized prompt, we could generate translations that preserved the original meaning and nuance, far surpassing the quality of context-unaware tools.

Human-in-the-Loop for Quality Assurance

To guarantee the highest level of accuracy and quality, all AI-generated translations were sent for a final review by human linguists. This crucial step ensured that every piece of localized content met the client's standards before being integrated into the live web application.