What is Retrieval Augmented Generation (RAG)? | Databricks

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM. RAG has shown success in support chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge. Here’s more to explore The Big Book of MLOps A must-read for ML engineers and data scientists seeking a better way to do MLOps. Tap the Potential of LLMs How to boost efficiency and reduce costs with AI. Databricks Named a Leader in New Report Databricks is a Leader in the 2024 Gartner®️ Magic Quadrant™️ for Data Science and Machine Learning Platforms. LLMs use deep learning models and train on massive datasets to understand, summarize and generate novel content. Most LLMs are trained on a wide range of public data so one model can respond to many types of tasks or que

What is Retrieval Augmented Generation (RAG)? | Databricks Skip to main content Summary Retrieval augmented generation is an AI pattern that improves large language model answers by first retrieving relevant documents from external data sources and then feeding that context into the model. RAG helps reduce hallucinations, keep responses up to date and tailor outputs to an organization's own content without retraining the underlying model. Common RAG use cases include customer support chatbots, internal knowledge search and augmented search experiences that answer questions directly from compan

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• Nguyen Hoang Thao

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