I Accidentally Built Enterprise AI Architecture
(And You Can Too)

What the Hell is RAG?

RAG stands for Retrieval-Augmented Generation.

It's when you give an AI system access to specific information or documents to reference, so it pulls relevant context and gives you better, more accurate answers instead of just relying on its training data.

It's what enterprises do.

Companies feed AI their internal documents, product specs, customer data, whatever — so the AI can answer questions specific to their business without hallucinating or making stuff up.

I didn't know any of this when I started. I just knew AI kept forgetting who I was.

The Problem: AI Has Amnesia

Here's what kept happening:

• I'd start a new chat and have to re-explain my background
• AI would suggest ideas that didn't fit my work style
• It would forget key details mid-conversation
• Long sessions would degrade — answers got worse over time

This was especially bad with Claude, which doesn't have persistent memory like ChatGPT. Every conversation started from scratch.

So I got annoyed and built a workaround.

The Solution: Feed It Context

I created a document that lives in Google Docs. It has:

• Who I am: Background, work history, skills
• What I'm working on: Current projects, goals, priorities
• How I work: Preferences, communication style, decision frameworks
• What I need help with: Specific use cases, recurring tasks

Every time I start a new conversation, I paste this document into the chat first.

Boom. Instant context.

Suddenly AI "remembers" who I am, what I care about, and how to help me.

What Changed

Once I started using my context doc, everything got better:

1. Better answers, faster

No more "I'm a product manager at a tech company" background every time. AI already knew my situation and could jump straight to helpful.

2. Suggestions actually fit my life

AI stopped suggesting generic advice and started tailoring to my specific context — my industry, my role, my constraints.

3. Continuity across tools

I use ChatGPT for some things, Claude for others, Gemini occasionally. The context doc means they all have the same baseline understanding of me.

4. Less mental overhead

I'm not re-explaining myself constantly. Copy, paste, go. It's like having a team member who actually remembers what you talked about last week.

Wait, This is RAG?

I built this system through trial and error. Zero courses. Just obsessive daily use and a lot of "well that didn't work, let's try something else."

Then I learned what RAG actually was.

Turns out I'd been building enterprise AI architecture in my free time.

Enterprises pay big money to implement RAG systems. They feed AI their internal knowledge bases so it can answer company-specific questions accurately.

I did the same thing, just for my own personal use. And it works.

How to Build Your Own Context Doc

You don't need to be technical. You just need to answer these questions in a Google Doc:

Section 1: Who You Are

• Your professional background
• Your current role and company
• Your key skills and expertise

Section 2: What You're Working On

• Current projects and priorities
• Goals for the next 3-6 months
• Challenges you're facing

Section 3: How You Work

• Your communication style and preferences
• Decision-making frameworks you use
• Tools and workflows you rely on

Section 4: What You Need Help With

• Recurring tasks AI can assist with
• Types of analysis or brainstorming you need
• Specific formats or outputs you want

Keep it updated. As your projects change, update the doc. As you learn new things about what works, add them.

The Bigger Lesson

Trial-and-error with AI tools is basically a self-taught MBA in how businesses will actually work with this stuff.

You don't need courses. You don't need certifications.

You need to use AI until it breaks, then figure out how to make it not break.

That's how you learn what actually works. That's how you build intuition. That's how you accidentally reinvent enterprise architecture.

And when companies start hiring people who "get" AI? They're not looking for people who took a course. They're looking for people who've been in the trenches, building things, breaking things, and figuring it out.