Can RAG handle unstructured data?

RAG handles unstructured data by breaking it into smaller, manageable chunks. These chunks are then converted into numerical vector representations (embeddings) using a language model. When a query is received, the system retrieves semantically relevant chunks based on their embedding similarity to the query embedding. Finally, a generative language model synthesizes an answer using the information from the retrieved chunks. Key considerations include the effectiveness of chunking strategies, embedding quality, and handling complex document layouts.

This capability makes RAG invaluable for creating AI applications that interact with diverse real-world information. Typical applications include intelligent document search systems, chatbots that answer questions using internal documentation, and knowledge bases summarizing vast amounts of textual content, significantly improving information accessibility.