Stop Vibe Coding: Context Engineering & RAG for AI Agents with Cole Medin

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Dec 22, 2025

In this conversation, Cole Medin, CTO of Automator and applied AI expert, dives into the intricacies of context engineering and Retrieval Augmented Generation (RAG). He emphasizes transforming casual 'vibe coding' into a structured approach to improve AI workflows. Cole shares practical tips on defining success criteria for projects, the importance of a solid planning phase, and advice for using AI to ask the right questions. He also discusses the value of communities in staying updated and introduces strategies for managing messy data efficiently.

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ADVICE

Use Embeddings And Supabase For RAG

Use an embedding model + vector DB (e.g., Supabase PGVector) to enable semantic search.
Return nearest embeddings as extra context to the LLM for better answers.

INSIGHT

Vectors Let RAG Scale

Vector search scales to very large corpora because queries compare proximity in vector space, not full scans.
You only retrieve nearby concepts, keeping context compact and relevant.

ADVICE

Filter Vectors With Metadata

Attach metadata to each vector to enable filtering, multi-tenancy, and hierarchical searches.
Use metadata to isolate clients or departments while keeping a single vector store.

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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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