Snowflake Invests in Voyage AI to Optimize Multilingual RAG Applications in the AI Data Cloud

Natural language is rapidly becoming the bridge between human and machine communication. But hallucinations — when a model generates a false or misleading answer — continue to be the biggest barrier to the adoption of generative AI. Retrieval-augmented generation (RAG) allows enterprises to ground responses from LLMs in their specific organization’s data, reducing hallucinations, improving contextualized understanding and improving explainability. This approach ensures that AI-powered applications built for specific business needs deliver responses that are accurate, relevant and reliable.

In a RAG application, embeddings — representations of real-world objects, like words, images or videos, in a format computers can understand — play a crucial role in converting the user prompt into a format the model can use to capture its semantic meaning. Ultimately, this helps the model generate a response to indicate the question can’t be answered or answer the user’s inquiry based on their organization’s data. 

In an era of globalization and the ongoing democratization of data insights, the ability to support conversational interactions in multiple languages is crucial. That’s why Snowflake Cortex AI, available in multiple cloud regions worldwide, is adding Voyage AI’s multilingual embedding model, which can be paired with multilingual LLMs from Meta, Mistral AI and more, to build these essential RAG applications.