9 powerful examples of retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) is a technique that enables large language models to access reliable and rich data sets, resulting in more accurate and personalized outputs. This involves retrieving relevant data from external sources, such as databases or documents, and feeding it to a language model, which uses the context to generate more informed responses. RAG has various applications, including providing personalized lead recommendations, helping employees prepare for interviews, generating actionable reports for sales leadership, and offering insightful feedback from sales meetings. By integrating with customers' systems and leveraging data from these integrations, companies can use RAG to deliver high-quality outputs and improve user experiences. Merge, a unified API solution, facilitates the adoption of RAG by allowing companies to add hundreds of integrations to their products through a single integration build, enabling machine learning models to utilize integration data across customer bases.

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