Retrieval-augmented generation (RAG), explained

Retrieval-augmented generation (RAG) enhances language models by retrieving relevant, current, proprietary, and verifiable information from external sources at query time, enabling responses grounded in evidence rather than relying only on knowledge encoded during training. A production RAG workflow typically ingests and parses enterprise content, preserves metadata and permissions, chunks and indexes material using embeddings, retrieves and reranks relevant passages, constructs prompts, generates answers, and may add citations, verification, and feedback loops. RAG differs from conventional search by synthesizing retrieved information into an answer, and from fine-tuning by changing the information a model can access rather than altering its behavior; the two approaches can be used together. Its reliability depends on source quality, retrieval precision, prompt design, access controls, and governance, since it can reduce but not eliminate hallucinations. Advanced forms include multi-hop retrieval across sources, self-reflective validation, agentic planning and tool use, multimodal retrieval for images, audio, and video, and structured-data retrieval from databases and APIs. The Box-focused section argues that effective enterprise RAG requires centralized, permission-aware, governed content, with tools such as Box AI, Hubs, Shield, Governance, APIs, and workflow automation intended to ensure secure retrieval, traceability, and use of generated outputs in business processes.

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