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What is RAG, and why it matters for business AI

A plain-English explanation of retrieval-augmented generation what it is, when it wins, and how to evaluate one.

January 20, 2025

If you've heard the acronym RAG and nodded along, you're not alone. It stands for retrieval-augmented generation, and it's the pattern behind most trustworthy business AI shipping today.

The one-paragraph version

A retriever finds the passages in your knowledge base that are most relevant to a question. A language model then answers using those passages and only those passages and cites the source. The user gets an answer they can verify; the business gets a system whose facts they can audit. The short-form definition sits in our glossary entry for RAG.

Why not just fine-tune a model?

Fine-tuning changes the model's weights. That's expensive to run, hard to update, and impossible to audit at the fact level. RAG leaves the model as-is and just feeds it your current documents. When you update a doc, the next answer reflects it no retraining.

Where RAG wins

  • Support and internal knowledge. Employees or customers keep asking the same knowledge-heavy questions. This is what our RAG systems service is built for.
  • Regulated content. You need to prove which source drove a claim.
  • Docs that change often. Product notes, policies, release notes.

Where RAG struggles

  • Genuinely novel synthesis. If the answer isn't in your corpus, a RAG system still won't invent it (which is a feature, but sometimes an inconvenience).
  • Highly conversational tasks with no source. Small-talk, negotiation, unstructured brainstorming.

The five things a production RAG needs

  1. A curated corpus. Not "everything on SharePoint" the sources people trust.
  2. Retrieval evaluation. Measured precision, recall, and answer accuracy on a real question set that grows over time.
  3. Access control. The retriever must respect existing permissions, not sidestep them.
  4. Source citation in every answer. No exceptions.
  5. A monitoring surface. So you know when quality drifts.

If the answers also need to do something (file a ticket, update a record, trigger a workflow), you probably want a RAG system paired with an AI agent, not RAG on its own.

If you're weighing RAG vs. a chatbot vs. a knowledge base, we're happy to sketch which fits your case.