Building a Language Translation App with LLMs and Transformers

Large language models have turned machine translation from a rules-based pipeline into a single API call. Modern multilingual transformers capture nuance, idioms, and domain-specific terminology that phrase-based systems miss. This guide shows you how to build a production-ready translation service using LLMs, with concrete code you can run today against Oxlo.ai.

Architecture of a Modern Translation Service

A minimal translation API has three layers: ingestion, inference, and post-processing. Ingestion accepts source text and a target language code. Inference sends a structured prompt to an LLM. Post-processing handles formatting, validation, and logging. Because LLMs are sensitive to prompt structure, the inference layer should include a system prompt that defines the translator persona, constraints on tone, and instructions for handling named entities.

For high-throughput applications, add a caching layer in front of the