Generative AI 101: What Is Retrieval-Augmented Generation?

Retrieval-augmented generation (RAG) is a technique that helps enhance large language models (LLMs) by providing context from external sources, such as databases or APIs. This improves the accuracy and relevance of LLM responses in generative AI applications. RAG combines three elements: generation, augmentation, and retrieval. Generation refers to working with an LLM without tailoring or prompt engineering. Augmentation adds detailed instructions for the LLM, while retrieval fetches information from external sources. By combining these elements, developers can create more accurate results from their prompts. RAG is particularly useful when building apps that require access to proprietary or real-time data not available in standard LLM training.

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