Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is a technique that improves the output of large language models (LLM) by using other information sources, often database or other corporate asset. RAG is powerful because it allows use of information that an LLM was not trained on, meaning results can reflect more recent data. As plenty of corporate data it highly specialized, RAG also means AI systems can more accurately represent a company’s own info instead of presenting generic results. Getting ready for RAG is not easy – it requires orgs to ensure their data – structured and unstructured – is ready to be ingested, a task that has prove difficult for decades.