RAG is the right architecture anywhere you need an LLM grounded in specific, private, or frequently-updated content. Here are the most common production use cases and the configuration decisions they require.
Users upload a document and ask questions about its contents. The document is chunked and embedded on upload; relevant passages are retrieved per question.
Small chunks (256–512 tokens). Citations required for verification. Strict faithfulness — model must not fill in information absent from the document.
Employee Q&A over internal docs (Confluence, Notion, SharePoint). Critical needs: access control via metadata filtering, freshness from continuous ingestion, multi-format parsing.
Product Q&A and troubleshooting using FAQs and runbooks. Critical needs: hybrid search (exact error codes + semantic), version metadata filtering, escalation when retrieved content is insufficient.
Legal document search — Search contracts, case law, and filings. Requires hybrid search (exact clause text + semantic), span-level citations, clause-aware chunking, and legal-domain embedding models.
Coding documentation assistants — API and SDK Q&A for developers. Requires code-aware chunking, version metadata filtering, and models that handle both code and prose.
Medical and scientific tools — Clinical and research Q&A. Requires verified sources only, strict faithfulness, mandatory citations, and specialized biomedical embedding models (PubMedBERT, BioLinkBERT).
Chunking strategy, embedding model, retrieval approach, re-ranking, metadata filtering, citations, and safety constraints all depend on what documents your users are querying and what questions they are asking. Always configure the system around the specific use case.
Ask the AI assistant about any of these RAG use cases or how to configure a RAG system for a specific domain.