---
type: source
title: "10 n8n Tips in 10 Minutes to 10x Your AI Automations"
description: "n8n is a powerful no-code AI automation platform, but the underutilized and trickier parts (production-grade memory/RAG stores, node referencing, looping, error handling, scheduling) are what separate toy workflows from scalable production ones."
youtube_id: Nsu9BzQv5C4
url: https://www.youtube.com/watch?v=Nsu9BzQv5C4
slug: 10-n8n-tips-in-10-minutes-to-10x-your-ai-automations
published: 2024-11-20
duration: "0:11:11"
recency_rank: 140
raw: "../raw/10-n8n-tips-in-10-minutes-to-10x-your-ai-automations.md"
tags: [n8n, no-code, automation, production, tips]
created: 2026-07-21
updated: 2026-07-21
---

# 10 n8n Tips in 10 Minutes to 10x Your AI Automations

Cole walks through ten fast, practical tips for building AI automations in [n8n](../entities/tools/n8n.md), focusing on the parts people trip up on rather than basic workflow construction, an exercise in [agentic workflow engineering](../concepts/agentic-workflow-engineering.md). His most emphasized point is production readiness: the default windowed buffer memory and in-memory vector store shown in most tutorials run inside the n8n instance and do not scale, so he recommends replacing both with [Supabase](../entities/tools/supabase.md) (Postgres chat memory plus the [pgvector store](../entities/tools/postgres-pgvector.md) for [RAG](../concepts/rag.md)) to stay on a single, scalable platform. He also gives a default LLM stack for quick starts, a practical [model-selection](../concepts/model-selection.md) cheat sheet: Claude 3.5 Sonnet ([Anthropic](../entities/organizations/anthropic.md)) for best quality, [Groq](../entities/organizations/groq.md)/Llama for speed, GPT-4o mini ([OpenAI](../entities/organizations/openai.md)) for cheap-but-capable, and text-embedding-3-small for RAG embeddings.

The middle tips are n8n mechanics that matter at scale as part of durable [no-code automation](../concepts/no-code-automation.md): different file types need different text-extraction nodes; referencing a previous node's output with json.* only works for the immediately preceding node, so you must reference a node by name to reach further back; and you can give an agent both a webhook node (as an API endpoint) and a chat trigger (for in-platform testing) by merging them with a small edit-fields expression that grabs whichever input is defined. He stresses careful handling of multi-item outputs, since n8n auto-loops over items one at a time and workflows must correctly handle cases like two files arriving simultaneously (e.g. concatenating both before sending to an LLM).

The final tips speed up development and harden workflows toward genuine [agentic automation](../concepts/agentic-automation.md): data pinning preserves a node's test output across reloads so you do not have to re-run test events; a dedicated error-trigger workflow wired in via a workflow's settings can send [Slack](../entities/tools/slack.md) alerts when anything fails, giving production monitoring; the schedule trigger runs workflows on an interval for time-based jobs like overdue-task reports; and the n8n template library offers 1000+ searchable starter workflows. [Google Drive](../entities/tools/google-drive.md) is among the integrations used in the examples.

## Concepts covered

- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Model Selection](../concepts/model-selection.md)
- [No-Code Automation](../concepts/no-code-automation.md)
- [Agentic Automation](../concepts/agentic-automation.md)

## Entities

- [n8n](../entities/tools/n8n.md)
- [Supabase](../entities/tools/supabase.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [OpenAI](../entities/organizations/openai.md)
- [Groq](../entities/organizations/groq.md)
- [Slack](../entities/tools/slack.md)
- [Google Drive](../entities/tools/google-drive.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:46]** AI-agent recommendations begin: avoid non-scalable in-memory buffer/vector stores
- **[0:01:34]** Tip 2: default LLM stack recommendation by use case
- **[0:02:33]** Tip 3: extract text from different file types with different nodes
- **[0:03:18]** Tip 4: referencing previous node outputs (json.* vs node-name reference)
- **[0:04:40]** Tip 5: webhook + chat trigger dual entry point for an agent
- **[0:05:52]** Tip 6: handling multiple items in a single node output (looping)
- **[0:07:11]** Tip 7: data pinning to persist test output across reloads
- **[0:08:08]** Tip 8: error workflows for production monitoring via Slack
- **[0:09:07]** Tip 9: schedule trigger for interval-based workflows
- **[0:09:53]** Tip 10: n8n workflow template library (1000+ templates)

## Transcript

[Raw transcript](../raw/10-n8n-tips-in-10-minutes-to-10x-your-ai-automations.md)
