AI Retrieval System
RAG Agent in n8n
An n8n workflow that pulls the right context from a source library before answering, so an assistant responds from actual reading instead of guessing.
The brief
What needed to be solved.
A normal chat flow falls apart when answers need to come from specific documents or stored knowledge.
The job was to build a workflow that could fetch the right context first, then answer from that context.
The constraint
What made it interesting.
I used n8n as the orchestration layer and treated retrieval as the center of the system.
The workflow pulls context, sends it into the model, and returns answers with better grounding.
The build
What was assembled.
n8n orchestration for request handling.
Retrieval step for pulling relevant source material.
LLM response stage built on the retrieved context.
Video-based project walkthrough for public review.
The result
What changed after it ran.
Made the assistant more reliable on source-based questions.
Showed how low-code tooling can still support serious AI work.
Added a clear RAG example to the portfolio.
Stack
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