Retrieval-augmented generation (RAG) has emerged as a leading method for combating hallucinations and other inaccuracies that affect large language model (LLM) content generation. However, the right data architecture is needed for RAG to scale effectively and efficiently.
Today, companies generate huge amounts of data from their intellectual property, operational procedures and marketing, sales and customer engagements. The biggest challenge is not collecting this data but rather extracting meaningful insights that can be used to…
