Stop Learning AI the Wrong Way
Let’s Build Customer Support the Way Real Systems Are Built
Welcome back to HosseiNotes 🚀
Let me say something mildly rebellious.
I don’t think most of us were taught how to learn properly.
School trained us to memorize first and apply later.
That’s backwards.
You only really understand something when you’re trying to solve a real problem.
When there’s friction. When something actually depends on it.
Without that?
We’re just collecting shiny tools like Pokémon cards. 🃏✨
And that’s not education. That’s decoration!
AI education right now is a perfect example.
You’re shown:
“Here’s RAG.”
“Here’s an agent.”
“Here’s today’s trending framework.”
Cool.
But what problem are we solving?
So instead of:
“Let’s learn RAG because LinkedIn said so…”
We’re going to solve something real:
Customer support chaos! 🔥
The messy inbox. The “where do I send this?” ping-pong.
The endless Tier-1 tickets that burn time and patience.
If you’ve ever looked at a support P&L, you know it’s ridiculously expensive, which makes it incredibly valuable to fix. 💼🔥
Why Customer Support Matters in 2026 📦
Customer support is margin compression.
Every ticket costs:
human time ⏳
context switching 🧠
SLA pressure 📉
internal Slack messages like “any update?” 😬
And here’s the quiet truth:
👉 70–80% of Tier-1 tickets are repetitive and automatable.
Password resets.
Billing confusion.
“Where’s my invoice?”
“I was charged twice.”
Not complex problems.
Just expensive ones.
Speed = CSAT (Customer Satisfaction).
CSAT = retention.
Retention = revenue. 💰
And no CFO in history has said:
“You know what we need? Slower support!”
Support is a cost center.
But good triage?
That turns chaos into leverage. 🚀
What We’re Actually Building 🏗️
We’re not building a chatbot.
We’re building a system that evolves.
And we’re not jumping straight to
“multi-agent quantum hyper-graph orchestration architecture v9.3” 🤯 (when all we needed was good triage 😁)
We climb the mountain properly.
Here’s the journey - fast and honest:
V0: Naive classifier - one prompt, feels magical… until it breaks.
V1: Chained reasoning - smaller steps, less chaos… surprisingly powerful.
V2: Intelligent routing - not every ticket deserves a human.
V3: Tool use - fetch real order data instead of hallucinating refunds. (Yes, that happens.)
V4: Memory - because users say “I tried that already.”
V5: RAG - grounded answers from real docs.
V6: Evals - because vibes are not metrics.
V7: Model-aware decisions - cost vs quality like grown-ups.
We’re not learning patterns for fun.
We’re learning them because the problem demands them.
Problem first.
Patterns second.
Always.
And Yes - You Can Build This for $0 💻
No excuses.
You can run this locally using Ollama and an open source model.
Example:
ollama run glm-5:cloud
Or from Python:
from ollama import chat
response = chat(
model='glm-5:cloud',
messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)
That’s it.
No API bill.
No vendor lock-in drama.
No “I’ll try this when procurement approves the budget.” 😅
If later you want hosted models? Great.
We’ll keep a LiteLLM abstraction layer so switching providers is painless.
Because again:
Models = suppliers.
Architecture = leverage.
The Real Point of This Series 🎯
This isn’t a LangChain tutorial.
It’s not “10 AI hacks to impress your manager.”
It’s not “How to build a chatbot in 7 minutes.”
It’s about building systems that survive contact with reality.
How do we:
turn messy text into structured decisions?
reduce support costs?
increase resolution speed?
prevent hallucinated nonsense from issuing imaginary refunds? 🧾❌
I don’t believe in memorizing APIs.
I believe in solving real problems end to end.
That’s how you build judgment.
That’s how you stop being impressed by shiny demos.
That’s how you start designing infrastructure.
What Happens Next 🔥
Next week, we start simple.
One prompt.
One classifier.
And then we improve it, iteration by iteration.
Because real systems aren’t built in one leap.
They’re refined until they create measurable value.
We’re not here to play with tech.
We’re here to leverage it.
See you next week. 🚀