Implementing RAG with Databricks: Efficient AI Enhancement

What you’ll learn

Discover the power of Retrieval Augmented Generation (RAG) with Databricks in our latest video, where we demonstrate the seamless integration of RAG to enhance large language model responses. This demo covers everything from data conversion to embedding models and efficient hosting with Databricks Model Serving, all while ensuring quality with continuous monitoring. Ideal for professionals in AI and data science, taking a RAG approach is great for those looking to elevate their AI applications with advanced, accurate information retrieval.

You will learn how to:

  • Prepare and clean documents to build your internal knowledge base and specialize your chatbot
  • Leverage Databricks Vector Search with our Foundation Model endpoint to create and store document embeddings
  • Search similar documents from our knowledge database with Databricks Vector Search
  • Deploy a real-time model using RAG and providing augmented context in the prompt
  • Leverage the llama2-70B-Chat model through with Databricks Foundation Model endpoint (fully managed)

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