What is retrieval augmented generation (RAG)?

Retrieval augmented generation (RAG) is a method that enhances large language models (LLMs) by providing them with relevant data at query time, allowing them to generate responses based on current and specific information rather than relying solely on their training data. This approach involves two sequential phases: retrieval, where a system searches a knowledge base to find relevant documents, and generation, where these documents are used as context in the LLM's prompt. RAG is particularly beneficial for automation tasks, as it reduces the risk of LLMs hallucinating incorrect facts, making it valuable for applications like customer support bots, internal research, and competitive analysis. Studies have shown that RAG-augmented systems significantly decrease factual errors compared to standalone LLMs. Platforms like CodeWords support RAG workflows through various integrations and tools, allowing for real-time, cost-effective, and contextually accurate AI responses. While fine-tuning is better suited for applications requiring specific writing styles or domain patterns, RAG is ideal for situations where factual recall and data freshness are critical, offering lower maintenance and hallucination risks.

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