14
Feb
Retrieval-augmented generation, often shortened to RAG, combines large language models with enterprise knowledge sources to produce responses grounded in authoritative data. Instead of relying solely on a model’s internal training, RAG retrieves relevant documents, passages, or records at query time and uses them as context for generation. Enterprises are adopting this approach to make knowledge work more accurate, auditable, and aligned with internal policies.Why enterprises are moving toward RAGEnterprises face a recurring tension: employees need fast, natural-language answers, but leadership demands reliability and traceability. RAG addresses this tension by linking answers directly to company-owned content.The primary factors driving adoption are:Accuracy…










