- RAG means the AI first retrieves relevant passages from your documents, then answers from them.
- It reduces made-up answers and lets you show the source of every answer.
- Its quality depends on the documents, how they are split and how access is controlled.
Many business AI tools use a technique called retrieval-augmented generation, or RAG. Leaders do not need to know the engineering, but understanding the idea helps you judge vendors and projects.
The idea in three steps
- Index: your documents are split into passages and stored in a searchable form.
- Retrieve: when someone asks a question, the system finds the passages most likely to contain the answer.
- Generate: the AI writes an answer using those passages, ideally citing them.
Why it matters
A general AI model answers from what it learned in training, which may be outdated or wrong for your company. RAG grounds the answer in your current policies, product data or contracts, and lets users click through to check the source.
What it needs to work well
- Clean, current documents with duplicates and old versions removed.
- Sensible splitting, so passages keep their context, such as tables with their headings.
- Access control, so users only retrieve what they are allowed to see.
- Testing with real questions and a process for fixing wrong answers.
When to use it
Use RAG for knowledge assistants, customer service based on policies, contract and document search, and any task where answers must reflect your own information. For pure drafting or brainstorming, it is not needed. See building an internal knowledge assistant.
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