3 RAG use cases—plus tips for implementing them

Retrieval-augmented generation (RAG) is a process enabling large language models (LLMs) to use context more effectively by embedding queries into vectors and identifying semantically similar data in vector databases to generate precise and informed outputs. This methodology is utilized by companies like Assembly, Juicebox, and Ema to enhance AI-driven features in their products, ranging from enterprise AI search solutions to AI-powered recruitment and universal employee agents. Effective implementation of RAG involves normalizing data to ensure consistency and accuracy, and using raw data for edge cases that require unique processing. Unified API platforms like Merge facilitate this process by providing a single integration build to access various software categories, allowing companies to manage customer integrations efficiently and support diverse RAG use cases.

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