Definition

Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant documents from a knowledge base and feeds them to a language model before generating an answer — reducing hallucinations and grounding responses in your own data.

Detailed Explanation

Pure LLMs answer from training data alone, which may be outdated or wrong for your business. RAG adds a retrieval step: embed your PDFs, Notion pages, or support tickets in a vector database; on each user question, fetch the top matching chunks; inject them into the prompt; then generate. The model cites (or should cite) retrieved context.

Production RAG requires chunking strategy, embedding model choice, access control, and evaluation — not just “upload all files to ChatGPT.”

Nepal Context

Nepali SMEs can use RAG for internal SOPs (pricing, service scopes) without sending client PII to public chatbots. Schools and microfinance institutions must respect data localization and NRB and privacy norms before customer-facing bots.

Agencies often start with RAG-lite: Google Drive folder + scheduled export + n8n LLM node — see n8n AI agents guide and privacy-first RAG stack.

Practical Examples

  1. Beginner: Paste three policy PDFs into a custom GPT for staff Q&A only.
  2. Intermediate: Notion wiki → weekly export → n8n summarizer for onboarding.
  3. Advanced: Vector DB (pgvector) + Ollama local model for offline support on LAN.

How to Check It

  1. Build a test set of 20 real questions with known correct answers and check the system’s replies against it after every change.
  2. Confirm each answer cites the document it came from, and that the cited passage actually says what the answer claims.
  3. Test access control: a user should never get an answer drawn from a document they are not allowed to read.

Key Takeaways

  • RAG grounds AI in your documents; it does not magically fix bad docs.
  • Start internal before customer-facing deployment.
  • Hybrid local + API models balance cost and privacy in Nepal.