How does AI use RAG to understand long documents

AI uses Retrieval-Augmented Generation (RAG) to understand long documents by first retrieving relevant snippets and then using that context to generate informed responses. This approach enables the AI to access information beyond what it was originally trained on.

RAG operates by breaking the long document into smaller chunks and converting them into numerical embeddings stored in a vector database. When queried, the AI finds the most relevant document chunks using semantic similarity search. These retrieved chunks are fed alongside the user's query into a generative language model. This context guides the model's response, grounding it in the source document. Accuracy depends on the quality of retrieval and the generative model's ability to interpret the context.

RAG is vital for applying AI to domain-specific, lengthy texts like manuals, research papers, or contracts. Implementation involves chunking documents, generating embeddings, establishing a retrieval mechanism, and integrating a capable generative model. This allows systems to provide specific, evidence-based answers drawn from the documents, enhancing information accessibility and reliability in specialized contexts.