Prompt engineering is chapter one. The real unlock is context engineering: a persistent, layered system that makes AI smarter every time you use it. Here's what that actually looks like.
New chat, type a question, get an answer, close the tab. Every conversation starts from zero. That's like hiring a brilliant assistant every morning who has never met you.
A great prompt in a vacuum gets a generic answer. The same prompt inside a system that knows your goals, voice, project history, and constraints? That's a genuine collaborator. I call this a context architecture — and building it is the highest-leverage skill in the age of AI.
Most people are on the left. The right is where things get wild.
Most people argue about which AI model is best. The people getting the most value aren't thinking about models at all. The model is the engine. Context is the fuel. A mediocre model with great context beats a frontier model with none.
Startup, MBA classes, personal CRM, content, research — all through Claude Code. Here are the 7 layers, starting from the simplest.
A file called CLAUDE.md in your home directory that loads automatically every session. It tells Claude who you are, what you're working on, and how you like to work. Mine is ~300 lines: a research index pointing to 80+ docs, active project map, working style preferences, and explicit rules like "never change pricing without asking me."
The power move: you can have multiple CLAUDE.md files. One at the root level (global context), one per project directory. Claude reads both. Your startup gets deployment safety rules. Your class project gets the assignment rubric. Same AI, completely different behavior.
# ~/CLAUDE.md (global — loads every session)
## Who Is Colton
- Founder & CEO of Rhythmicly (AI sleep tech)
- Dual MBA/MPH at UC Berkeley Haas
- Former Amazon PM
- Berkeley, CA — Pacific timezone
## Working Style
- Prefers direct communication
- Bias toward action over analysis
- When you notice perfectionism, call it out:
"You've thought about this enough. Decide and ship."
## Critical Rules
- Never change pricing without asking
- Never modify production configs without confirmation
Personal context documents: my values, 2026 goals, career vision. Not instructions for Claude — context about me as a person. When it helps me make a strategic decision or draft a pitch, it understands not just what I want to do, but why.
This is the "soul" layer. The difference between "help me write a LinkedIn post" and "help me write one that sounds like me, reflects my actual beliefs, and connects to what I'm building this year."
When I give feedback ("don't mock the database in tests"), Claude saves it as a memory file. Meeting details, project decisions, corrections — all persisted. Tomorrow's session knows what today's session learned.
The AI gets better over time not because the model improved, but because the context did. I have memories for preferences, project context, feedback corrections, and pointers to external systems.
# Example memory file
---
name: Writing Voice
description: How Colton's content should sound
type: feedback
---
- Experience over authority. Show, don't claim.
- Sensory language over abstract concepts.
- Specific numbers = credibility.
- Sound like a smarter version of Colton, not a
corporate communications team.
I run 2-3 Claude Code terminals simultaneously. Without coordination, they'd duplicate work or miss context from each other. The Command Center solves this: a dashboard (2-minute overview), weekly sprint files, a review queue, and daily logs. Every session reads the dashboard first. Every session logs what it did. Cross-session awareness, zero manual coordination.
An execution tier system decides what Claude does autonomously (research, deploying), what it drafts for my review (emails, content), and what only I can do (meetings, filming). The system knows which is which.
Saved workflows invoked with slash commands. /meeting-prep pulls CRM history, checks calendar, and generates a brief. /content-plan reads analytics and drafts the next week. /investor-prep builds a full dossier.
I have ~20 of these. Each encodes domain knowledge that would take 10 minutes to explain. Instead: one command, it just runs. The skill files are plain markdown — you teach Claude a workflow in natural language, not code.
# Some of my skills:
/meeting-prep → CRM lookup + calendar + brief
/meeting-debrief → Save notes, update CRM, create tasks
/content-plan → Analytics + series framework + drafts
/investor-prep → Investor dossier + warm paths + angles
/new-activation → Build a personalized activation page
/handoff → End-of-session state transfer
/brand-voice → Load voice guidelines for content
Specialized sub-versions of Claude for specific domains:
Each agent has its own instructions, model selection (analyst = most powerful, CRM = fastest), and tool access. They run as sub-processes returning results to the main session.
Everything above lives inside Claude Code. This layer connects to the outside world. Through MCP servers, Claude directly accesses Google Calendar, Gmail, Drive, Sheets, n8n automation workflows, iOS Simulator (tap buttons, take screenshots, test features), and meeting transcripts.
On top of that, 14 automated workflows run on n8n Cloud: content generation Saturdays, ops digests Mondays, daily anomaly alerts, hourly CRM syncs, queue aging warnings.
This is where AI stops being a tool you open and becomes infrastructure running in the background. Always on.
My system took months to build. Yours will look completely different. What matters is the principle: context compounds. Every layer makes every other layer more valuable.
Start with Layer 1. Five minutes. Add layers as you feel the edges of what's possible.
Your existing AI already knows a lot about you. The fastest way to build Layer 1: have it write a summary, then paste it into Claude Code.
Open whichever AI you use most (ChatGPT, Claude.ai, Gemini) and paste this:
You know a lot about me from our conversations. I'm going to start using a new AI coding tool and I need to give it context about who I am.
Write a comprehensive summary of everything you know about me. Cover:
1. Who I am — name, background, what I do
2. What I'm currently working on and building
3. My goals for the next 6-12 months
4. How I like to work and make decisions
5. My communication style and preferences
6. Things I care deeply about
7. Anything else that would help a new AI work with me effectively
Make it specific, personal, and honest — not a generic template. This is going to be read by an AI that's never met me, so include the things that matter most.
Review what it generates, edit anything that's off, paste it into Claude Code, and tell it: "Remember all this stuff about me." It saves to memory and loads in every future conversation. Months of accumulated context, ported in 5 minutes.