State-of-the-art hybrid search for RAG and AI App

From the beginning, Snowflake’s mission has been to empower customers to extract more value from their data. In the era of enterprise AI, this mission extends more than ever to unstructured data, where RAG has become a standard approach to customizing generative chat applications with proprietary data. RAG empowers organizations to create, among many other things, powerful customer service, sales and R&D applications that accurately leverage their proprietary data.

Yet, while retrieval is a fundamental component of any AI application stack, creating a high-quality, high-performance RAG system remains challenging for most enterprises. Consider the components one must manage to successfully deploy RAG at scale:

• Infrastructure and operations: Platform teams have to deploy and manage numerous retrieval components — hosted embedding models, vector databases, data indexing pipelines, hosted reranking models, observability tools and more.
• Search-quality tuning: Engineers and data scientists have to spend time evaluating models and parameter configurations to tune the retrieval and ranking components to their specific business use cases.
• Security and governance: Security teams have to conduct extensive reviews to ensure that each component in the stack is treating data securely and respecting governance policies.