RAG, or retrieval-augmented generation, is the practical way to make an AI assistant answer questions from your own documents instead of making things up. The idea is simpler than the acronym suggests.
Retrieve first, then generate
When a user asks a question, the system finds the most relevant passages from your documents and gives them to the model as context. The answer is grounded in your content, not the model's guesswork.
Quality depends on the retrieval
- Clean, well-structured source documents matter more than the model.
- Chunk content sensibly so passages stay coherent.
- Always show the source so users can verify the answer.
Start narrow
Point RAG at one well-understood set of documents, like a support knowledge base, before expanding. A focused assistant is reliable; a sprawling one is hard to trust.
Abishek Bimali
Founder & Engineer
Abishek founded SiteCraft Innovation and leads its engineering. He writes about building web and mobile products that hold up in production, for teams in Nepal and abroad.
