We leverage large language models (LLMs) for our multi-language support. Our approach includes: 1. API integration: We use state-of-the-art LLMs via API calls,

We leverage large language models (LLMs) for our multi-language support. Our approach includes: 1. API integration: We use state-of-the-art LLMs via API calls, which provide robust multilingual capabilities out of the box. 2. Prompt engineering: We've developed language-specific prompts to optimize the LLM's output for social media contexts in different regions. 3. Post-processing: We apply rules-based filters to ensure cultural appropriateness and platform-specific formatting. 4. Human-in-the-loop: For less common languages or sensitive content, we incorporate human review to maintain quality. 5. Continuous evaluation: We regularly benchmark the LLM's performance across languages and fine-tune our prompts based on user feedback and engagement metrics. 6. Fallback mechanisms: For languages where LLM performance is suboptimal, we fallback to traditional machine translation APIs with additional post-processing. Current challenges include managing API costs at scale and ensuring consistent brand voice across languages. We're exploring fine-tuning options on smaller, more efficient models for frequently used language pairs to address these issues. We currently support the languages covered by our LLM provider, with plans to expand as their capabilities grow.