---
type: source
title: "I Built a FULL RAG AI Agent with No Code in 4 Minutes with VectorShift"
description: "For simple use cases or fast proofs of concept, a no-code drag-and-drop platform like VectorShift can build a full, working RAG AI agent in minutes instead of coding one from scratch."
youtube_id: ieLdMih5_V0
url: https://www.youtube.com/watch?v=ieLdMih5_V0
slug: i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift
published: 2024-09-27
duration: "0:10:07"
recency_rank: 160
raw: "../raw/i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift.md"
tags: [no-code, rag, vectorshift, rapid-prototyping]
created: 2026-07-21
updated: 2026-07-21
---

# I Built a FULL RAG AI Agent with No Code in 4 Minutes with VectorShift

Cole frames AI-agent development as a spectrum: on one end, complex, highly specific needs justify coding an agent from scratch; on the other, simple use cases or quick proofs of concept are better served by low-investment no-code platforms so you don't reinvent the wheel. He picks VectorShift, a drag-and-drop component workflow builder (comparable to n8n but AI-focused) to build a full RAG agent in under five minutes.

The build has three pieces: Knowledge (a knowledge base connected to a Google Drive folder of meeting notes, ingested as vectors with default chunk size and embedding model), Pipelines (the visual workflow), and Chatbots (the deployment surface). In the pipeline he wires an Input node into an LLM node (Anthropic Claude 3.5 Sonnet) using a prompt with two variables, the user input and a context variable, then adds a Knowledge Base reader node that queries the KB with the input and feeds retrieved results back as the LLM's context, ending in an Output node. He deploys, tests with a question only answerable from the ingested notes, and gets a correct grounded answer fast.

He closes by showing deployment options: export the pipeline as an automation, chatbot, search, or form, surface it as a hosted URL, an API for a custom frontend, or a Slack integration. The Google Drive connection stays continuously in sync so the knowledge base updates as files change. Prebuilt templates (for example a multi-LLM blog-article generator) let you start without building from scratch. His takeaway: there's a real time and place for these platforms when custom code isn't worth it.

## Concepts covered

- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Knowledge Bases](../concepts/knowledge-bases.md)
- [Vibe Coding](../concepts/vibe-coding.md)
- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [Chunking](../concepts/chunking.md)
- [Vector Embeddings](../concepts/vector-embeddings.md)

## Entities

- [n8n](../entities/tools/n8n.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [Google Drive](../entities/tools/google-drive.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** The spectrum of agent development: code-from-scratch vs. fast no-code proof of concept
- **[0:00:30]** Introduces VectorShift as a drag-and-drop AI workflow builder, compared to n8n
- **[0:02:24]** Creates a knowledge base and connects a Google Drive meeting-notes folder, ingested as vectors
- **[0:03:31]** Starts building the pipeline from scratch with an Input node
- **[0:03:58]** Adds an Anthropic Claude 3.5 Sonnet LLM node with input plus context prompt variables
- **[0:04:59]** Adds the Knowledge Base reader node, feeding retrieval results as LLM context
- **[0:06:23]** Deploys and tests: correct grounded answer to a notes-only question in seconds
- **[0:07:20]** Publishes/exports the agent as chatbot, API, URL, or Slack integration
- **[0:08:36]** Google Drive folder stays continuously in sync with the knowledge base
- **[0:08:49]** Prebuilt templates (for example a multi-LLM blog generator) as a starting point

## Transcript

[Raw transcript](../raw/i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift.md)
