AI & Automation
RAG Explained for Business Leaders
RAG lets AI answer questions using your own documents. Here is how it works in plain English.
Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents for relevant information, then uses a language model to write an answer based on what it found — ideally with citations. It lets AI assistants answer accurately about your products, policies and data without retraining a model.
How RAG works
- Documents are split into chunks.
- Chunks are converted into searchable embeddings.
- A question retrieves the most relevant chunks.
- The language model writes an answer using them.
- The answer cites sources.
Business use cases
- Internal knowledge assistants for policies and procedures.
- Customer support answers grounded in help articles.
- Sales enablement using product documentation.
- Contract and document search.
Benefits
| Benefit | Why |
|---|---|
| Accuracy | Answers grounded in your data |
| Freshness | Update documents, not models |
| Transparency | Citations show sources |
Risks to manage
- Poor document quality leads to poor answers.
- Access control: users should only see what they are allowed to.
- Evaluation is needed to measure accuracy.
Hire AI developers to build RAG systems.
