18/10/2025
đ¨ Alert! Game Changer For Your Customer Support System.
A few weeks ago, a client asked me something simple:
âCan we make our chatbot actually understand our business data â not just answer from the internet?â
That question led me down a rabbit hole. And the result?
I built a RAG-based (Retrieval-Augmented Generation) customer support chatbot â powered by N8N, OpenAI, and Pinecone Vector Database.
Hereâs what makes it special đ
Every time a new document is added to Google Drive, the workflow automatically:
1ď¸âŁ Reads and splits the document into small text chunks.
2ď¸âŁ Converts them into embeddings (a numerical form of meaning).
3ď¸âŁ Stores them inside Pinecone, a vector database built for semantic search.
Now, when a customer asks a question, the chatbot doesnât guess â it retrieves the most relevant data from your companyâs knowledge base and then generates a precise, contextual response.
No hallucinations. No generic answers. Just real, data-backed support â available 24/7.
This is the same principle used by advanced AI assistants â and now, itâs possible to build one visually using N8N, without heavy coding.
I call it a RAG-backed AI Support Agent â a system that grows smarter as your documents grow.
If youâd like me to break down the full workflow step-by-step (and explain how you can build it too), drop a âRAGâ in the comments đ