The n8n Masterclass
Welcome to The n8n Masterclass. I’m Dylan Watkins.
Each week we break down real business wins using n8n and AI automation. You’ll learn what worked, why it worked, and what broke along the way. Founders, automation experts, and AI agencies share how they use n8n to simplify operations, scale delivery, and move faster without adding more people.
The n8n Masterclass is not about tutorials or tech jargon. It focuses on the real business side of automation, the frameworks, decision patterns, and creative problem solving that turn workflows into results.
You’ll leave every episode with one principle, one pattern, and one action you can apply this week to grow your business, reduce manual work, and unlock the potential of automation.
If you’re building a business that runs on smart systems, AI, and no-code tools, this is your playbook for scaling with n8n. Follow The n8n Masterclass and start using automation as your competitive edge.
The n8n Masterclass
Stop Vibe Coding: Context Engineering & RAG for AI Agents with Cole Medin
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In this episode, we sit down with Cole Medin, CTO of Automator and expert in applied AI, to dive deep into context engineering, RAG (Retrieval Augmented Generation), and building scalable AI workflows. Cole shares practical strategies for cutting through the noise in the AI space, designing effective prompts, and moving from prototypes to production-ready systems. Whether you’re an AI builder, developer, or just curious about the latest in automation, this conversation is packed with actionable insights and real-world advice.
00:00 – Introduction: The challenge of too much “fluff” in the AI space and how to focus on what matters.
00:22 – Meet Cole Meine: Background, expertise, and his mission in applied AI.
01:59 – What listeners will learn: Context engineering, RAG, and moving workflows to production.
02:40 – The origin of context engineering: Why treating prompts and context as engineered resources matters.
03:49 – Vibe coding vs. context engineering: The importance of specificity and reducing assumptions.
06:18 – Practical steps for context engineering: Mindset shift, planning, and using AI to ask clarifying questions.
08:47 – Success criteria and user journeys: How to define what “done” looks like for AI projects.
12:36 – How much time to spend on planning: Product requirement docs and upfront investment.
13:54 – Favorite AI coding tools: Cloud Code, Codex, and Google’s Anti-Gravity.
15:23 – Staying up to date in AI: Research strategies and the value of community.
18:09 – Introduction to RAG (Retrieval Augmented Generation): What it is and why it matters.
20:41 – How RAG works: Embedding models, vector databases, and semantic search.
24:45 – Metadata filtering in RAG: Multi-tenancy, hierarchical search, and business use cases.
28:46 – Handling messy data: ETL/ELT pipelines and preparing data for AI agents.
32:06 – Scaling workflows: Moving from n8n prototypes to production code (Python/TypeScript).
34:38 – Deployment strategies: Frontend, backend, and cloud hosting options.
37:13 – The importance of version control: Using GitHub for safe states and CI/CD.
40:05 – Final advice: Start simple, build your process, and customize your system.
41:15 – Where to find more: Cole Meine’s YouTube channel for more on RAG and context engineering.
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