Retrieval-Augmented Generation (RAG)
Pattern that grounds language-model answers in retrieved documents — typically more reliable than fine-tuning for institutional search.
In long form.
Retrieval-Augmented Generation combines a search step with a generation step: the system first retrieves relevant documents from a vector store or search index, then passes them to a language model along with the user's question. The model's answer is grounded in the retrieved context rather than relying on parametric memory. RAG is generally more reliable, more current, and easier to update than fine-tuning when the goal is institutional search or document-grounded answers.
Most 'AI search' features for institutional knowledge bases — internal wikis, support docs, regulatory archives — are RAG implementations. Fine-tuning is appropriate when style or task format must change; RAG is appropriate when the underlying facts must change.
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