Understanding RAG: Building Retrieval-Augmented Generation Systems
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating company-specific data, such as policies and manuals, to produce accurate and relevant responses. This method mitigates hallucinations by ensuring responses are grounded in verified content.
To build effective RAG systems, companies should segment data into 5-8 business domains, capping each at 1,000 to 10,000 rows instead of relying on a single datastore. This improves accuracy as terminology can vary significantly across departments. Human validation from domain experts is crucial for refining responses, alongside adding metadata to enhance search precision.
For optimal results, organizations should empower individual departments to create and manage their RAG systems. This approach fosters agile, self-sustaining adoption of generative AI technologies, leading to what we term the “democratization of RAG development.” Effective collaboration between business units and IT teams is essential for ongoing improvements.
