I go solo on this one to break down graph engineering, the term I keep seeing go viral on X. I define it in plain English: prompt engineering is how you ask AI a better question, context engineering is how you give AI better information, and graph engineering is how you design the work around the AI so it lives as a managed workflow instead of one giant chat. I walk through the vocabulary (jobs, arrows, state), separate knowledge graphs from agent graphs, and run a full worked example on whether to launch an AI bookkeeping product for Shopify merchants. Then I show three levels of implementation, from manual lanes on a whiteboard up to LangGraph and n8n, plus ready-made graphs for support, content, and code. You leave with a repeatable way to turn one AI workflow you already run into a map of steps, checks, handoffs, loops, and human approvals.



Timestamps

00:00 – Intro

01:24 – Prompt Engineering, Context Engineering, Graph Engineering

02:50 – Chat vs Graph

03:35 – Defining Terms and Workflows

06:44 – Knowledge Graphs vs Agent Graphs

08:47 – When to use Graph Engineering

10:01 – Example: AI Bookkeeping For Shopify Merchants

13:22 – The Diamond Pattern Graph Visualized

15:10 – Three Levels of Implementation

17:14 – Customer Support Graph

18:45 – Content Creation Graph

19:30 – Coding Graph

20:42 – The Trap Of Oversized Graphs

22:22 – Building Your First Graph

24:53 – Closing Thoughts



Key Points





Graph engineering means designing the work around the AI: jobs connected by arrows, with shared state moving between them.



Knowledge graphs help AI understand how information connects; agent graphs help AI understand how work should move.



Reserve a graph for work with multiple steps, multiple sources, parallel paths, checks, risks, or approvals.



Separate the writer from the checker, since a single model grading its own answer inflates confidence.



Draw and run the graph manually first; add LangGraph, n8n, or Make com once the structure proves itself.



Aim for the smallest graph that raises quality, and place the human gate where mistakes get expensive.



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