Retrieval-Augmented-Generation (RAG) has quickly emerged as a powerful way to incorporate proprietary, real-time data into Large Language Model (LLM) applications. Today we are excited to launch a suite of RAG tools to help Databricks users build high-quality, production LLM apps using their enterprise data.
LLMs offered a major breakthrough in the ability to rapidly prototype new applications. But after working with thousands of enterprises building RAG applications, we’ve found that their biggest challenge is getting these applications to production quality. To meet the standard of quality required for customer-facing applications, AI output must be accurate, current, aware of your enterprise context, and safe.
To achieve high quality with RAG applications, developers need rich tools for understanding the quality of their data and model outputs, along with an underlying platform that lets them combine and optimize all aspects of the RAG process. RAG involves many components such as data preparation, retrieval models, language models (either SaaS or open source), ranking and post-processing pipelines, prompt engineering, and training models on custom enterprise data. Databricks has always focused on combining your data with cutting edge ML techniques. With today’s release, we extend that philosophy to let customers leverage their data in creating high quality AI applications. Click here to continue reading!
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