RAG use cases

Retrieval-augmented generation is well-suited to applications requiring accurate answers grounded in specific documents — including enterprise knowledge bases, legal and regulatory research, customer support over product documentation, technical support systems, and any application where hallucination risk must be managed by grounding answers in verified source material.

Where RAG adds the most value

RAG delivers clear value in applications where three conditions hold: the answers must be grounded in specific documents rather than general knowledge, the document corpus is too large to fit in a single context window, and accuracy on domain-specific facts matters more than fluency on general topics. Enterprise knowledge management — letting employees ask questions answered in internal documentation, policies, and procedures — is the canonical use case. Legal and compliance research, where answers must be traceable to specific regulations and case references, benefits from RAG's ability to cite source material. Technical product support, where answers must match the actual behavior of a specific product version rather than general technical knowledge, is another high-value application.

RAG in customer-facing applications

Customer support applications use RAG to answer questions about specific products, services, and policies by retrieving from support documentation rather than relying on the model's general knowledge. The grounding reduces hallucination — a common failure mode in customer service AI where plausible-sounding incorrect answers create customer harm. Medical and healthcare information services use RAG to anchor responses in vetted clinical guidelines, reducing the risk of confidently stated but inaccurate medical information. Financial services applications use RAG to ground responses in current product terms, regulatory guidance, and company policy.

Where RAG is the wrong tool

RAG works poorly when the answer requires synthesizing across the entire corpus rather than retrieving specific passages — questions like 'what are the three most important themes across all these documents' require a different architecture. It also struggles with questions that require reasoning rather than retrieval: a question that needs logical inference across multiple retrieved facts is better handled by an agent architecture that can iterate over retrieved information. And RAG is unnecessary for applications where the model's general training knowledge is sufficient — adding a retrieval layer increases latency and cost without benefit when the model already knows the answer reliably.

RAG use cases — FAQ

Can RAG be used with structured data like databases?

Yes, but with different retrieval mechanisms. Standard RAG retrieves from unstructured text using semantic similarity. For structured data, SQL generation — prompting a model to write a database query from natural language — is often more appropriate than embedding-based retrieval. Hybrid approaches retrieve relevant schema information and example rows from the database to ground SQL generation, which is a form of RAG adapted to structured data.

How do I handle documents that are frequently updated in a RAG system?

The document index needs to be updated when source documents change. Strategies range from full re-indexing on a schedule (simple but potentially stale between runs) to incremental indexing that detects changed or new documents and updates only affected embeddings. The right approach depends on how frequently documents change and how quickly stale information in the retrieval index becomes a problem for your use case. Very frequently updated sources — real-time feeds, live databases — are often better served by direct query tools rather than embedding-based retrieval.