How a RAG chatbot works
- Prepare content: clean and split documents into useful sections with source metadata.
- Create a searchable index: convert sections into embeddings and store them for vector search.
- Retrieve context: use the question to find relevant sections.
- Generate an answer: give the retrieved context and clear instructions to the language model.
- Show evidence: return citations or source links so users can verify important answers.
Where it can help
Customer support
Search product documentation, setup instructions and approved FAQ material while linking to the original source.
Internal knowledge
Help employees find policies, procedures, manuals and team documentation across approved files.
Website discovery
Guide visitors to relevant services, product information or technical content using published pages as sources.
Document review
Locate relevant sections across PDFs and reports, with access rules appropriate to the source material.
What RAG does not solve automatically
RAG can still return incomplete or incorrect answers. Retrieval quality depends on source documents, chunking, metadata and query handling. Sensitive content needs authentication and authorization. Production systems also need evaluation questions, failure handling, monitoring, cost controls and a path to human support.
Questions to answer before development
- Which documents and formats are in scope?
- How frequently does the content change?
- Must different users see different documents?
- Should answers include citations?
- Which languages and integrations are required?
- How will answer quality be tested?