Retrieval-augmented generation (RAG) is a way of giving an AI model access to specific information, like your company's documents, when it answers questions.
The problem it solves
AI models only know what was in their training data. They don't know your internal policies, product manuals or latest updates, and may guess when asked.
How RAG works
- Index: your documents are split into chunks and stored in a searchable database (often using "embeddings" that capture meaning).
- Retrieve: when someone asks a question, the most relevant chunks are found.
- Generate: those chunks are given to the AI along with the question, so it answers based on your content.
Benefits
- More accurate, up-to-date answers
- Answers can cite the source document
- No need to retrain the model when content changes
Common uses
Customer support bots, internal knowledge assistants, legal and research tools, and product documentation search.
Limits
RAG is only as good as the documents and retrieval. Outdated or messy content leads to poor answers, so keep sources clean.