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RAG Agent in n8n project cover
AI Retrieval System RAG Agent in n8n

AI Retrieval System

RAG Agent in n8n

Built an n8n RAG workflow that pulled the right context before answering, so the assistant could respond from actual source material.

The system answered from context instead of guessing.

n8nRAGLLMKnowledge Workflows
Retrieval QA Mode
n8n + LLM Tooling
Knowledge access Focus

Problem

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.

Approach

How the system was framed.

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.

Build Details

Architecture, tooling, and operating logic.

  • 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.

Results

Operational outcome.

  • 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.